By 2050, sub-Saharan Africa’s (SSA) urban population is expected to grow from 414 million to over 1.2 billion. This growth will likely increase challenges to municipalities attempting to provide access to water supply and sanitation (WS&S). This study aims to characterize trends in access to WS&S in SSA cities and identify factors affecting those trends.DHS data collected between 2000 and 2012 were used for this analysis of thirty-one cities in SSA. Four categories of household access to WS&S were studied using data from demographic and health surveys – these included: 1) household access to an improved water supply, 2) household’s time spent collecting water, 3) household access to improved sanitation, and 4) households reporting to engage in open defecation. An exploratory analysis of these measures was then conducted to assess the relationship of access to several independent variables.Among the 31 cities, there was wide variability in coverage levels and trends in coverage with respect to the four categories of access. The majority of cities were found to be increasing access in the categories of improved water supply and improved sanitation , while fewer were making progress in reducing the amount of time spent collecting water and reducing open defecation . Additionally, the prevalence of open defecation in study cities was found to be, on average, increasing.Based on DHS data, cities appeared to be making the most progress in gaining access to WS&S along metrics which reflect specified targets of the Millennium Development Goals. Nearly half of the cities, however, did not make progress in reducing open defecation or the time spent collecting water. This may reflect that the MDGs have led to a focus on “improved” services while other measures, potentially more relevant to the extreme poor, are being neglected. This study highlights the need to better characterize access, beyond definitions of improved and unimproved, as well as the need to target resources to cities where changes in WS&S access have stalled, or in some cases regressed. It has been estimated that sub-Saharan Africa’s (SSA) urban population will nearly triple by 2050, increasing from 414 million to over 1.2 billion [Due in large part to rapid urban population growth, the number of urban SSA residents using the worst forms of sanitation or water supply has increased between 1990 and 2010. In that time, the number of people using untreated surface water as their primary source of water grew from 5 to 9 million, and those whose primary sanitation method was open defecation increased from 16 to 25 million . This isIt is estimated that most urban population growth in SSA will occur in informal settlements or slums. Informal settlements are difficult to define and there is substantial diversity in conditions between and within cities. Typified by congestion and poor access to vital resources, such as WS&S, residents of these areas often see health outcomes much worse than non-slum dwelling urban residents and even rural inhabitants . A 2006 The urban poor of SSA, who are often residents of informal settlements, have much lower rates of access to both WS&S. Sixty four percent of the poorest quintile of urban residents have access to improved water supply compared to 94% of the wealthiest quintile. Similar disparities are seen in sanitation with 42% of the poorest quintile using improved or shared sanitation compared to 91% of their wealthy counterparts .Given these rapid rates of urbanization and stagnating rates of access to WS&S in urban areas in SSA, further examination of this area is critical. Identifying the current levels of access in individual cities of SSA, in which direction they are heading, and identifying the driving factors will become more important as cities grow larger and struggle to provide these basic services to their residents. The aims of this study were to 1) estimate the average annual change in access to WS&S in the largest cities across SSA from 2000-2012; 2) explore how city-level and country-level factors may affect progress towards increasing access to these basic WS&S services; and 3) identify and discuss the limitations of measuring trends using demographic and health survey data.Publicly available demographic and health survey (DHS) data for Sub-Saharan Africa were used to conduct this research. All surveys included in this analysis were conducted from the year 2000 and later. DHS are nationally-representative household surveys where standard surveys are usually conducted every three to five years. These surveys provide information on various topics including housing characteristics and household health. The Measure DHS program implements a standard model questionnaire approach in order to collect data that is comparable across countries (DHS Questionnaires 2012). Each country has surveyed anywhere from 5,000 to 30,000 households in each survey. The sample is representative at a national, residence, and regional level. For cities, the sampling frame is defined by that country’s census bureau. Sampling is typically based on a stratified two-stage cluster design. The first stage uses census files to identify enumeration areas (EA), small administrative units with defined boundaries and a known population size. Most surveys select 300-500 EAs with probability proportional to population size. In the second stage, an updated listing of households in each selected EA is used from which sample households are drawn. Data are collected by a team consisting of six to eight field workers who travel to conduct interviews and enter data onto either paper questionnaires or electronic files on the computer (DHS Methodology 2012).Selection criteria for cities to be included in the study were: 1) a population size greater than 1 million defined by UN:DESA; 2) to be located in a low- or lower-middle income country of SSA as defined by The World Bank; and 3) DHS data sets were available for two time points within 2000-2012 [see Additional file The study included four measures of access, which were estimated based on demographic and health surveys (DHS) for each country:• Percent of households with access to an improved water supply• Percent of households with access to improved sanitation• Percent of households that spend 30 minutes or more collecting water• Percent of households reporting to engage in open defecationIn selecting these measures of access to WS&S, two indicators were selected to mirror the development benchmarks of improved access set by the MDGs. The DHS surveys indicate drinking water source and type of sanitation for each household. Data from the DHS were categorized as improved or unimproved based on WHO/UNICEF Joint Monitoring Programme definitions of improved water supply and sanitation . The finTo further explore access to WS&S, each access variable was assessed and analyzed in two ways. First, the most recent estimate of the percentage of households with access was determined using DHS data from the most recent year for each city. Second, the annual change in the percent of households with access was calculated using a line equation based on the available DHS data within the study period (2000-2012).In developing independent variables to conduct an exploratory analysis, the literature was reviewed to understand which factors could potentially be linked with access to WS&S. The independent variables in this study can be categorized as demographic, socio-economic, political, and environmental, and all the data used were publicly available [see Additional file 2, was obtained from Demographia World Urban Areas 2012 [City population statistics and urbanization rates were obtained from the UN Department of Economic and Social Affairs’ World Urbanization Prospects, The 2011 Revision. Every two years since 1988, UN:DESA has released revised estimates of the urban and rural populations of every country and their major urban agglomerations. As data are collected on urban agglomerations of 750,000 people and greater in the UN:DESA report; all the cities included in this study have data available [eas 2012 .GINI coefficients, which consist of a mixture of income and consumption based measures, were used to measure the city-level income inequality. GINI coefficients were obtained from UN Habitat’s Global Urban Indicators Database . The GloJacobsen et al.[A quantitative indice of wealth for the cities analyzed was not available. Similar to other studies, such as en et al., nationaen et al. – these en et al..Data for the average level of education were obtained from each city’s most recent DHS. The measure for education was the percentage of heads of household in each city that completed secondary school.Official Development Assistance (ODA) for water supply and sanitation, measured in millions of dollars per year averaged over the study period of 2000-2012, was obtained from the Organization for Economic Co-operation and Development (OECD). While unable to parse out data for the individual cities of this study or even to the urban level nationally, this study used data on funds allocated to “water supply and sanitation – large systems” as a proxy for ODA in urban areas. Large systems are defined by OECD for water supply as: potable water treatment plants, intake works, storage, water supply pumping stations, and large scale transmission/conveyance and distribution systems; and for sanitation as: large scale sewerage including trunk sewers and sewage pumping stations, domestic and industrial wastewater treatment plants . To compData on flood occurrence were obtained from the World Research Institute’s Aqueduct Global Water Risk Mapping Tool . The tooThere are important limitations to using these indices for the analysis. Some of the data used for our independent variables were national level indices (e.g. per capita GDP), while the WS&S access measures were city-level. Further, it was unclear where the official development assistance for WS&S was targeted and whether the cities in our analysis received support. Given the limitations of the exposures of interest, the analysis should be considered exploratory in nature only.The development of descriptive statistics for WS&S access – city-level prevalence of open defecation or the average annual percentage point change in open defecation, for example – were the main focus of the analysis. Exploratory data analysis using simple linear regression was also conducted to look at the associations between the independent variables and the four measures of WS&S access. Independent variables were first analyzed individually with the annual change of each WS&S access indicator, or dependent variable, using a bivariate linear regression model. Multivariate regression analysis was also conducted to control for the other independent variables in measuring association. Of the 31 cities that met the initial selection criteria, several were omitted from certain categories of the assessment [see Additional file In assessing the four measures of access to WS&S through DHS data, significant variability was observed among the study cities. There was also substantial variability in how cities progressed in their ranking across the four categories. Some cities were found in similar relative position across categories, for example, while others were near the top in some categories and near the bottom in others.Major differences among the cities were observed for the average annual change in access among the study’s four categories of access. The majority of cities were found to be making at least minimal progress in the categories of improved water supply and improved sanitation , while fewer of the study cities were found to be making progress in time spent collecting water and open defecation .Based on the DHS data, 3 of the 31 cities were either making progress or approaching universal coverage across all four measures of access: Lagos, Dakar, and Douala. A further 8 cities made progress in three of the measures of access: Nairobi, Dar es Salaam, Ouagadougou, Bamako, Conakry, Kampala, Benin City, and Kumasi.None of the study cities were found to be regressing across all four measures of access to WS&S, and only three were found to be regressing in three of the four: Abuja, Kaduna, and Harare.Of the cities analyzed, 26 had sufficient DHS data to calculate the most recent coverage levels for improved water supply during the study period of 2000-2012. The average coverage level for improved water supply for the 26 cities was found to be 91.7%. As shown in Figure As a whole, the 26 cities were generally increasing access to water supply according to the data, with an average improvement of 0.7 percentage points annually during the study period. With many cities approaching 100% coverage, their rates of change were more likely to be close to zero as they neared this ceiling. Even with that mitigating factor, there remained a wide range of cities’ average annual change. The city with the greatest progress over the study period was Benin City with an average annual improvement of 4.7 percentage points. Conversely, Abuja’s coverage level was found to be declining the fastest, decreasing 1.7 percentage points annually. In addition to Abuja, four more cities were found to have decreasing coverage levels: Kaduna, Harare, Accra, and Lusaka. Additionally alarming were Abuja and Kaduna’s regressive trends combined with already low coverage levels, both in the bottom four cities for most recent improved water coverage levels.Ten cities had coverage levels that were making minimal or no progress in either direction : Yaoundé, Lagos, Mombasa, Kampala, Antananarivo, Dakar, Ouagadougou, Abidjan, Maputo, and Douala. While preferable to regressive trends, these stagnating coverage levels in addition to population growth mean that the actual number of people without access to improved water supply is increasing in many of these cities. The remaining ten cities, as well as the aforementioned Benin City, made significant progress during the study period : Luanda, Kano, Dar es Salaam, Niamey, Conakry, Bamako, Kumasi, Nairobi, Brazzaville, and Douala.To further measure the access to water supply in the study cities, the amount of time households reported collecting water was analyzed. Of the study cities, sufficient data were available for 22 of the cities. Of those 22 cities, the data indicated that the average proportion of households reporting 30 or more minutes to collect water was 16.6%, but there was again a wide range seen between the cities. Shown in Figure In addition to Yaoundé having the greatest proportion of households reporting 30 or more minutes collecting water, this proportion was found to have increased at an annual rate of 1.6 percentage points during the study period. There were six other cities that saw significant annual increases  in this measure: Abuja, Addis Ababa, Kaduna, Kampala, Mombasa, and Accra with Abuja’s 4.3 percentage point increase the most dramatic. Four cities made no significant change over the study period: Kano, Dar es Salaam, Nairobi, and Niamey. Half of the 22 cities saw decreases in this collection-time measure over the course of the study period: Ouagadougou, Harare, Douala, Benin City, Antananarivo, Lagos, Dakar, Conakry, Bamako, Lusaka, and Kumasi. This many cities heading in the right direction might indicate overall progress in this measure, but the average change in proportion for the 22 cities was just -0.1 annual percentage points.When looking at the two measures of water access together, there are some interesting results. Addis Ababa, Douala, and Ouagadougou had some of the strongest coverage levels of improved water supply, but all ranked as some of the lowest performing cities when looking at the measure for collection-time. It appears in these locations that although households are using better sources of water, that they are having to travel farther or wait longer in lines to utilize them.Shown in Figure Although overall coverage levels for improved sanitation were relatively low for the 23 cities, the data indicate that they are making good progress. The average annual percentage point change across the study period was an increase of 1.6%. Additionally, only three cities’ coverage level regressed significantly (less than -0.5 percentage points): Brazzaville, Antananarivo, and Harare. Three cities were found to have no significant change (between -0.5 and 0.5): Addis Ababa, Benin City, and Abidjan. As stated in regards to water supply, these stagnating coverage levels are preferable to regression, but combined with population growth lead to a growing population of people without access to improved sanitation facilities.The data suggest that 17 cities made significant progress in increasing coverage levels of improved sanitation during the study period. Some cities made significant progress; Kano and Abuja had annual percentage point increases of 9.3% and 5.2%, respectively. Seven other cities increased by more than 2 percentage points each year: Lusaka, Kaduna, Mombasa, Yaoundé, Nairobi, Douala, and Dakar. The remaining eight cities’ coverage levels grew between 0.5 and 2 percentage points annually during the study: Kumasi, Dar es Salaam, Conakry, Kampala, Accra, Bamako, Lagos, and Ouagadougou.Reducing the worst form of sanitation, open defecation, is a vital step in improving sanitation in cities. Prevalence of open defecation provides a measure of sanitation access that highlights the poorest and most vulnerable city dwellers.Of the study cities, 26 cities had sufficient data to calculate prevalence of open defecation and annual rates of change. Typically seen in rural areas, prevalence of open defecation was fairly low in the 26 cities, with an average of 3.4% households reporting the practice in the most recent DHS surveys. Two cities, Dar es Salaam and Maputo, had no households reporting open defecation in the most recent survey, and a further ten cities had a prevalence that was below 1%: Nairobi, Abidjan, Luanda, Conakry, Dakar, Bamako, Harare, Douala, Yaoundé, and Kampala. Eight more cities had rates below 3%: Antananarivo, Kano, Accra, Lusaka, Mombasa, Ouagadougou, Lagos, and Brazzaville. Prevalence of open defecation takes an alarming upturn in the remaining six cities: Kumasi (3.7%), Addis Ababa (5.8%), Kaduna (9.2%), Niamey (10.7%), Abuja (16.0%), and Benin City (22.9%).Unlike the progress seen in coverage of improved sanitation, the study cities’ prevalence of open defecation is actually increasing. According to the data, the average annual percentage point increase for the 26 cities was 0.3%. As shown in Figure The exploratory data analysis portion of this study was limited to a select number of factors, and was merely an exploratory look into which factors are associated with progress on WS&S in the largest cities of SSA.Across the nine independent variables, little significance was found with any of the measures of access to WS&S. No independent variable was found significantly correlated with more than one measure of access, and the majority (7 independent variables) were found not to be significantly correlated (at a 90% confidence level or higher) with any of the measures of WS&S access.The three factors that were found to be significantly associated with any of the measures of access were city population density, national level GDP growth, and official development assistance for water supply and sanitation, large systems. It was found that denser cities were associated with higher annual increases in access to improved sanitation during the study period. Cities with higher national level GDP growth were associated with greater improvements of improved sanitation. Finally, a reduction in the prevalence of open defecation was associated with higher levels of ODA for WS&S.Data for all four measures of access and the independent variables are available in the summary table  by wealth quintile. Such an analysis could perhaps shed light on the role wealth might play in this disparity.The results linking population density to average annual change in sanitation coverage suggested that, perhaps contrary to what might be expected, the most densely populated cities are making the greatest progress in increasing coverage levels of improved sanitation.The analysis of economic growth variables yielded some interesting results. While economic growth was unquestionably a vital part of governments ability to provide essential services to their people, these results did little to reinforce the notion that either national level GDP or GDP growth leads directly to increased access to WS&S in large cities, particularly in water supply, time collecting water, or open defecation. The results of this analysis indicated that GDP growth may be more critical in providing sanitation than in the other measures of access. ODA, as with all the independent variables of the study, was also found to have an insignificant relationship across three of the four measures of access, only being found positively correlated with decreases in the prevalence of open defecation. What was most striking about this finding was that the proxy of “large scale WS&S systems”  used for development assistance targeting urban WS&S would not necessarily be expected to affect households practicing open defecation. One hypothesis for this unexpected correlation involves perhaps the more obscure effects that ODA may have. While the assistance itself, in a monetary sense, might not reach those populations practicing open defecation, the resources can create WS&S-related job positions and bring WS&S to the fore politically. This was an interesting finding given that ODA is often considered ineffective, and that access to these vital services come naturally from economic growth.While DHS and other nationally representative surveys are likely the most useful data for comparing cities in terms of WS&S access, it is important to highlight their limitations. One major limitation was how DHS may fail to include populations living in informal settlements. If the boundary of the sampling frame does not include peri-urban informal settlements, for example, then these communities will be underrepresented. This limitation likely varies from city to city and restricts the conclusions that can be drawn when comparing cities in this analysis. Additionally, for each of these cities, informal populations are rapidly changing (most often increasing), which has an effect on the accuracy of the trends that can be assessed for each city from year to year. Furthermore, as DHS looks to improve its surveying and how to better include these marginalized populations, future surveys will lose comparability with previous ones. A feature of the DHS surveys that was not utilized by this study was the GPS data that accompanies the household surveys. A thorough geospatial analysis accompanying the analysis of this study would likely have led to a better understanding of what, if any, populations of each city were underrepresented.In analyzing factors that drive access to WS&S, particularly water supply, it might be argued that water resources are a major constraint. While there is an obvious link between domestic water supply and water resources, evidence suggests that domestic water use represents a minor fraction of water withdrawals . AdditioMuch debate continues as to whether shared or public toilets can be considered improved sanitation. The WHO/UNICEF Joint Monitoring Programme, for the purposes of the MDGs, classifies any shared or public sanitation facility as unimproved, and therefore this study did the same. Due to an inability to measure actual use or the hygiene and safety of shared sanitation facilities, the JMP currently considers all these facilities unimproved, even though most shared facilities may use an improved technology . StudiesFor this analysis, the use of shared facilities varied greatly between the study cities. In classifying every household that used shared facilities as having unimproved access, there were likely shared facilities that would meet improved hygienic and safety criteria, but were still classified as unimproved. The use of shared sanitation facilities is on the rise in developing countries, and while the JMP’s dichotomous classification of improved/unimproved eases measurement, it is currently and will only further become an incomplete measure.Identification of who has access to improved WS&S is a complex process. In this estimation, many factors of access are not taken into account. This is particularly true for water supply for which measures of water quality, quantity, cost, seasonal changes in access are not taken into account by the DHS survey. Failure to account for these aspects likely overestimates access to what might be considered a sufficient water supply, particularly for urban residents.Studies have shown that although many urban households may be classified as having ‘improved’ access to drinking water, the quality and quantity of this water remains poor or inadequate. In densely populated urban areas, a household may be close enough to use a standpipe as their source of water (a piece of hardware classified as ‘improved’), however, they may share that standpipe with dozens of other households, limiting the amount of water truly available to them .The results of this study underscore the complexity of attempting to define access to both water supply and sanitation. The need to create dichotomous variables of improved and unimproved WS&S can be useful in creating development goals such as the MDGs, but in doing so, the subtleties of access are lost.With the current MDGs set to expire in 2015, there is a need to incorporate the many aspects of WS&S access into the measures used in the post-2015 development agenda. For water supply these aspects include quality, quantity, cost, time spent collecting water, and seasonal fluctuations. For sanitation, along with identifying shared facilities that might be adequate, issues of cost, distance from the home, and seasonality need to be considered as well.The primary limitations to this study were the lack of reliable city-level data and the use of independent variables, of varying quality, from different data sources than the dependent variables. Different data sets measure cities differently , which can limit the conclusions drawn from the findings.Due to inadequate city-level data, this study explored how national level indices could affect access. Rather than omit important variables such as GDP, GDP growth rates and official development assistance, national level data were used as proxy measures for city-level economic conditions. This use of national-level data as proxies to estimate city-level data has been used in other studies dealing with the same dearth of city-level data .For each city included in this study, only two surveys within the 12-year study period were used to assess progress in access to WS&S. If the surveys were conducted early in the decade, then the changes to access observed may not represent the city’s most recent progress. The difference in when the demographic and health surveys were conducted for each country, shown in Table S3, would likely affect the changes in access observed for that particular city.Finally, while this study examined the largest cities of sub-Saharan Africa, these cities are not a complete representation of “urban sub-Saharan Africa”. In the entire continent of Africa (including Northern Africa), cities of more than one million people only accounted for 31.6% of Africa’s total urban population in 2010. Additionally, of the 63.8 million-person increase in the urban population of Africa between 2005-2010, only 27% of that was in cities of over one million .Despite these limitations, this study had several strengths. Previous reports, such as the UN HABITAT’s State of African Cities 2010 Report and the World Bank’s Future of Water in African Cities report, have presented city level data on access to water supply and sanitation. The World Bank report analyzed individual cities along 45 different criteria . This stThere was substantial variability between the cities studied in the levels of access within each of the four measures of access. Variability of access was also seen within cities themselves across the four categories of access. Major differences among the cities were also observed for the average annual change in access among the study’s four categories of access. The majority of cities were found to be making at least minimal progress in the categories of improved water supply and improved sanitation, while fewer of the study cities were found to be making progress in time spent collecting water and open defecation.As shown in Figures Given the exploratory nature of the analysis of driving factors, it was difficult to draw conclusions about how the different factors included in this analysis affected access to WS&S. More effort is needed to understand the factors that influence access to WS&S in cities of low-income countries. As national surveys, such as the DHS, improve their methods over time, they will be able to better characterize marginalized urban inhabitants, and better be able to assess access levels as well as trends. Improved DHS data coupled with reliable city-level data for other factors, will allow for more rigorous statistical analysis to hypothesize the drivers of access.As cities continue to grow in number and size throughout sub-Saharan Africa, it will become increasingly important to identify trends in access to WS&S and the potential driving factors. This understanding will allow officials to more effectively target resources. For example, local municipalities, as well as international development agencies, could use this knowledge to tailor strategies to address negative trends in accordance with the specific circumstances of each city. It will also be important that the multiple levels of access – not just ‘improved’ and ‘unimproved’ access – be considered to ensure that a more complete characterization of access is determined. Finally, improving our understanding of city-level performance in terms of driving factors and access trends could facilitate the exchange of knowledge and sharing of best practices between cities and may result in more effective investment of external funds, including official development assistance and concessionary loans.AMCOW: African minister’s council on water; DHS: Demographic & health survey; FWAC: Future of water in Africa (Report); GDP: Gross domestic product; JMP: Joint monitoring programme; MDGs: Millennium development goals; OD: Open defecation; ODA: Official development assistance; OECD: Organization for Economic Co-operation and Development; SSA: Sub-Saharan Africa; STHs: Soil transmitted helminths; UN: United Nations; UN:DESA: United Nations Department of Economic and Social Affairs; UNICEF: United Nations Children's Fund; WASH: Water, sanitation and hygiene; WHO: World Health Organization; WRI: World Research Institute; WS&S: Water supply and sanitation.The authors declare that they have no competing interests.MH and JG collaborated in designing the study. MH conducted the data analysis and drafting of the paper. JG offered support in data analysis and edited several iterations of the paper. Both authors read and approved the final manuscript.The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1471-2458/14/208/prepubDHS datasets used for each city included in the study.Click here for fileSupplemental materials.Click here for fileSummary of data used for the analysis.Click here for file


Oryza series Sativae species in Asia Pacific: Oryza nivara Sharma and Shastry, O. rufipogon Griff., and O. meridionalis Ng. Genetic similarities between O. nivara and O. rufipogon across their distribution are evident in the clustering and ordination results and in the large proportion of shared alleles between these taxa. However, local-level species separation is recognized by Bayesian clustering and neighbor-joining analyses. At the regional scale, the two species seem more differentiated in South Asia than in Southeast Asia as revealed by FST analysis. The presence of strong gene flow barriers in smaller spatial units is also suggested in the analysis of molecular variance (AMOVA) results where 64% of the genetic variation is contained among populations (as compared to 26% within populations and 10% among species). Oryza nivara (HE = 0.67) exhibits slightly lower diversity and greater population differentiation than O. rufipogon (HE = 0.70). Bayesian inference identified four, and at a finer structural level eight, genetically distinct population groups that correspond to geographic populations within the three taxa. Oryza meridionalis and the Nepalese O. nivara seemed diverged from all the population groups of the series, whereas the Australasian O. rufipogon appeared distinct from the rest of the species.Genetic variation patterns within and between species may change along geographic gradients and at different spatial scales. This was revealed by microsatellite data at 29 loci obtained from 119 accessions of three  Oryza rufipogon and O. nivara. These two closest relatives of Asian cultivated rice (O. sativa L.) are morphologically distinct  as well as O. rufipogon populations from insular Southeast Asia. These three taxa, along with cultivated rice compose Oryza series Sativae in the Asia-Pacific area.In an effort to answer these questions and uncover underlying spatial variation patterns, this study analyzes locally sympatric accession pairs  of O. nivara and O. rufipogon; (2) infer geographic population groups in Asia-Pacific Oryza series Sativae; and (3) assess genetic diversity at the population group and species level.For this study we use simple sequence repeat (SSR) markers to: (1) determine global-, regional-, and local-scale differentiation between O. nivara and O. rufipogon and of O. meridionalis and O. rufipogon across their distribution range, as well as O. rufipogon populations that are nonsympatric to both annual species (O. sativa and a wild species) . Six O. sativa accessions were also included for comparison  at the International Rice Research Institute (IRRI) in the Philippines were selected to represent sympatric populations of  species . Due to  species  wherein species) . We inclmparison . The plaGenomic DNA was extracted from fresh leaf samples by applying the modified CTAB (cetyl trimethyl ammonium bromide) extraction protocol . However, RM514 did not amplify well in most of the samples and was dropped from the analysis.The markers used Table  were fro2 free buffer, 10 mmol/L deoxynucleotide triphosphates, and 25 mmol/L MgCl2 , 0.08 μL of 5U/μL i-Taq™ DNA polymerase (iNtRON Biotechnology), 1 μL of 1 μmol/L labeled M13 forward primer , 1 μL of 1 μmol/L M13-tailed SSR forward primer , 2 μL of 1 μmol/L SSR reverse primer (Invitrogen), and 4 μL of genomic DNA. The program started with denaturation at 95°C for 2 min, succeeded by 32 cycles of denaturation at 95°C (30 sec), annealing at 55°C (30 sec) and elongation at 72°C (50 sec), then followed by a 2 min final extension step at 72°C. The annealing temperature was adjusted to match the optimal value for each marker as indicated in Table Polymerase chain reaction (PCR) was conducted in 20 μL reaction volume composed of 5.92 μL sterilized ultrapure water, 2 μL each of 10× MgClPCR products were multiplexed by combining 2 μL each of IRDye 700- and IRDye 800-labeled samples, 5 μL sterilized nanopure water, and 5 μL loading dye. Gel electrophoresis was performed on the 4300 LI-COR DNA analyzer system. The LI-COR IRDye 50–350 bp size standard ladder was used to estimate allele size. The gels were analyzed and scored with the SAGA Generation 2 software .FIS) were obtained using PowerMarker 3.25 , frequency of major allele , observed heterozygosity, unbiased estimate of gene diversity Weir , polymorA cluster analysis was conducted with PowerMarker 3.25  of C.S. Chord distance matrices between species across distribution was performed with GenAlEx 6.4  values were selected from each K and their average ln P(D) were used in calculating the delta K (ΔK), a statistic based on the rate of changes in the likelihood distribution between successive K values  model of admixture  was implemented with a linear trend surface. The maximal number of clusters was set to range from Kmax = 2 to Kmax = 10. Each Kmax was tested with 100 runs, and each run had 20,000 burn-in sweeps followed by another 30,000 sweeps. To determine the appropriate number of TESS clusters, the deviance information criterion (DIC) should be analyzed and the stability of the bar plots should be considered , the 10 STRUCTURE runs with maximal ln (P[D]) values as well as the 10 TESS runs with the lowest DIC values were aligned and averaged using CLUMPP 1.1.2 . The output files (bar graphs) were viewed and edited with the DISTRUCT software  were detected at the 29 SSR loci. The number of alleles per locus varied from three (RM455) to 32 (RM316), with an average of 14. The number of rare alleles ranged from zero (RM495) to 28 (RM316), with an average of nine. The most common allele in each locus had a mean frequency of 0.39 and varied from 0.13 (RM431) to 0.93 (RM338). The PIC values differed from 0.13 (RM338) to 0.91 (RM237 and RM271) and had a mean value of 0.69  and only the O. meridionalis and Nepalese O. nivara clusters have strong bootstrap support . Oryza nivara and O. meridionalis join in a large cluster where the latter forms a distinct group that seems closer to the Southeast Asian populations of the former. The Nepalese O. nivara forms a separate branch within the admixed cluster. The aromatic and japonica populations of O. sativa group with several South Asian accessions of O. rufipogon, whereas the Australasian population joins a cluster composed of both South and Southeast Asian O. nivara. Three O. nivara populations from India  and an intermediate population from Sri Lanka (N37) separated from the annual species and form a cluster. Three O. nivara accessions  group with O. rufipogon while the intermediate populations are distributed among different clusters.The NJ tree Fig.  shows thO. nivara and O. rufipogon  and 2 (19.10%). Oryza sativa accessions are distributed throughout the plot with aromatic and japonica populations joining O. rufipogon, Australasian, and one indica accession grouping with O. nivara, and the other indica accession in the middle of the O. nivara – O. rufipogon complex  separates the Nepalese O. nivara from the rest of the taxa. The succeeding principal coordinate axes displayed uninformative clustering patterns.The first two principal coordinate axes reflect separate but partially overlapping clusters of gon Fig. A. Oryza K (from K = 2 to K = 8) obtained by CLUMPP  than STRUCTURE as indicated in the average membership coefficients of each y CLUMPP . Across K plot of the STRUCTURE runs displays distinct peaks at K = 2 , K = 4, and K = 6  Southeast Asian O. nivara; (C3) continental and insular Asian O. rufipogon; and (C4) O. meridionalis and Australasian O. rufipogon ; two clusters in O. rufipogon (a South Asian group joined by the aromatic and japonica accessions of O. sativa and a Southeast Asian cluster in 40% of the runs); and three clusters in O. nivara . Nevertheless, the output of the six-cluster solution of STRUCTURE (and even TESS) seems unstable because certain populations  appear fragmented and/or are swapped between different clusters , display less stable clustering and do not recognize additional distinct population clusters aside from the groups inferred at K = 8  Indian and Bangladeshi O. nivara; (C2) Cambodian O. nivara; (C3) continental and insular Asian O. rufipogon; (C4) O. meridionalis; (C5) Nepalese O. nivara; (C6) non-Cambodian O. nivara; and (C7) Australasian O. rufipogon  that is split into the Southeast Asian O. rufipogon (C3 of K = 8) and South Asian O. rufipogon (C8 of K = 8) clusters  at K = 4 seems dubious as the genetic uniqueness of O. meridionalis detected by PCoA is not recognized in the said cluster solution  comprise the O. rufipogon group  and C5  form distinct clusters (O. rufipogon) from the rest of O. rufipogon at K = 7 and K = 8  exhibit higher FIS values (0.94–0.98) than the perennial taxa (0.85–0.87)  Table . Among tO. rufipogon) is the most diverse among the population groups, followed by C8 (South Asian O. rufipogon)  and greater than those of South Asian O. nivara clusters C1 and C5. Next to C4 , C5  shows the least diversity among the population groups. Heterozygosity is greater in the O. rufipogon clusters C3, C7, and C8 (0.07–0.1) than in the rest of the population groups (0.01–0.03). Clusters with the highest proportion of rare alleles are C3 (53.5%), C8 (41.6%), and C2 (35.3%)  Table . The genOryza meridionalis shares two alleles with O. nivara and five alleles with O. rufipogon. In stark contrast, O. nivara and O. rufipogon share 192 alleles making up more than half of the total alleles detected in the annual (68.6%) and perennial (56.1%) taxa. Of the 192 alleles, 14 are exclusively present in Southeast Asian populations , only one allele is endemic to South Asian populations (C1 and C8), whereas the remaining 177 alleles are not restricted to regionally sympatric populations  are shared by nonsympatric populations  are found in locally sympatric populations and 141 are found in nonsympatric populations  shared by ulations . Among tulations .Oryza rufipogon has the largest proportion of unique alleles , followed by O. meridionalis  and O. nivara   discriminates a small proportion  of one or two O. rufipogon population group/s. No allele is present throughout the distribution range of O. nivara. The 39 unique alleles from 20 markers discriminate at least one of the four O. nivara population groups in frequencies ranging from 0.007 to 0.304  ; Fig. 7.ulations . RM44, Rto 0.304 .Oryza series Sativae (excluding O. sativa) resides mainly among accessions  and to a lower degree within accessions (26%) as well as among the three species (10%) (Table ΦPT = 0.74) and between species (ΦRT = 0.1), respectively (both at P < 0.001 level).Based on the AMOVA results, genetic variation in Asia-Pacific %) Table . SignifiK = 8 display different degrees of differentiation  to 0.58 . Oryza meridionalis is clearly the most distinct population group. The mean pairwise FST value between O. nivara clusters (0.23) is greater than between O. rufipogon clusters (0.13) and even between clusters of O. nivara and O. rufipogon (0.19) suggesting deep genetic divisions within the annual species. Oryza nivara from Nepal (C5) is clearly differentiated from the rest of the clusters. The Australasian cluster C7 seems the most distinct among the O. rufipogon population groups. The pairwise FST values are lower between the Southeast Asian clusters of O. nivara (C2 and C6) and O. rufipogon (C3) than between the South Asian clusters of the two species.The population clusters identified by TESS at O. nivara and O. rufipogon across their distribution range, concurring with results of previous molecular studies , even earlier than the recognition of O. meridionalis as a distinct group (at K = 3).NJ Fig.  and ordis Second . None of regions  renders  regions . HoweverOryza nivara and O. rufipogon populations from the same locality cluster apart from each other (except N43 and R43) in the NJ tree. Spatially explicit Bayesian clustering differentiated the two species in sympatric population pairs throughout their distribution range  displaying a clinal variation pattern. This China–South Asia division cannot be confirmed in the present analysis as only one accession from China was sampled.The results confirmed the genetic disparities of es Figs. , 2, 8 anes Figs. , 5, 8. Tes Figs.  where O.O. rufipogon. Genetic and morphological differentiation between continental and insular populations has been reported in many other plant species .The genetic distinctiveness of  NJ Fig. , ordinat NJ Fig. , and FSTFST Fig.  results.O. nivara to inbreeding depression and genetic erosion. More in-depth studies are needed not just to confirm the unique genetic identity of these regional populations but also to further establish variation patterns that will aid in formulating in and ex situ conservation strategies.Low diversity and genetic isolation from the rest of the species expose the Nepalese O. rufipogon with O. sativa var. japonica and of O. nivara with O. sativa var. indica , the japonica and aromatic varietal groups join up with O. rufipogon, whereas the indica and aus groups do so with O. nivara  (Banaticla-Hilario K = 6) as a genetic admixture of O. nivara and O. rufipogon, whereas the other populations were included in the O. nivara–indica group. Caution should be taken when interpreting SSR diversity patterns as the presence of interaction between cultivated and wild taxa could be masked by the genetic similarities within Oryza series Sativae. Vaughan et al. (It is worth mentioning that of the six populations morphologically classified as weedy forms (i.e., intermediate between n et al.  warned tO. nivara and O. rufipogon, less so in the geographically restricted O. meridionalis. The recognition of local differentiation in the midst of global similarities reconciles the conflicting results of prior studies (Second O. nivara and O. rufipogon varies at different geographic scales.This research imparts a more detailed account of the genetic variation patterns in s Second . FurtherO. meridionalis, O. nivara in Nepal, and O. rufipogon in Australasia call for immediate conservation measures. Furthermore, the vast amount of genetic variation detected among populations justifies the maintenance of a large collection of Asian wild rice germplasm.The revealed geographic partitions within species as well as the inferred population groupings within the series can be considered in assessing the genetic representativeness of genebank collections and in selecting plant materials for in and ex situ conservation and research purposes. Especially, the uniqueness and vulnerability to genetic degradation of


Tetracladium marchalianum. We sampled populations of this fungus from seven sites, three sites each in two rivers in Illinois, USA, and one site in a Wisconsin river, USA, and repeatedly sampled one population over two years to track population genetic parameters through two seasonal cycles. The resulting fungal isolates (N = 391) were genotyped at eight polymorphic microsatellite loci. In spite of seasonal reductions in the abundance of this species, genotypic diversity was consistently very high and allele frequencies remarkably stable over time. Likewise, genotypic diversity was very high at all sites. Genetic differentiation was only observed between the most distant rivers (∼450 km). Clear evidence that T. marchalianum reproduces sexually in nature was not observed. Additionally, we used phylogenetic analysis of partial β-tubulin gene sequences to confirm that the fungal isolates studied here represent a single species. These results suggest that populations of T. marchalianum may be very large and highly connected at local scales. We speculate that large population sizes and colonization of alternate substrates in both terrestrial and aquatic environments may effectively buffer the aquatic populations from in-stream population fluctuations and facilitate stability in allele frequencies over time. These data also suggest that overland dispersal is more important for structuring populations of T. marchalianum over geographic scales than expected.Aquatic hyphomycete fungi are fundamental mediators of energy flow and nutrient spiraling in rivers. These microscopic fungi are primarily dispersed in river currents, undergo substantial annual fluctuations in abundance, and reproduce either predominantly or exclusively asexually. These aspects of aquatic hyphomycete biology are expected to influence levels and distributions of genetic diversity over both spatial and temporal scales. In this study, we investigated the spatiotemporal distribution of genotypic diversity in the representative aquatic hyphomycete  In the case of aquatic hyphomycete fungi, which are microscopic, submerged in water, and grow into and through opaque substrates, population genetic studies can generate unprecedented insight into otherwise unobservable biological phenomena.Within-species genetic diversity is distributed spatially among populations and is variable over time. Levels and distributions of this diversity can be assessed using data from molecular markers .In this study, we use one of the most commonly occurring Tetracladium marchalianum is only known to reproduce via mitosis, either through the production of distinctive tetraradiate haploid spores .d spores  or fragmd spores  has 1–3 Quercus (oak) and Acer (maple) common at all sites, and Platanus (sycamore) also present at the Illinois sites.The three rivers sampled in this study were the Middle Fork of the Vermilion (Vermilion) and Sangamon Rivers in Illinois, USA, and Konkapot Creek in Wisconsin, USA . The VerT. marchalianum spores were observed, maximally one month. Individual spores were isolated from the dishes and allowed to germinate on antibiotic water agar [AWA: 1.8% Bacto Agar  plus 0.5 mg/ml Streptomycin Sulfate and Penicillin G  per 1 L deionized water] in Petri dishes. The identity of each germinating T. marchalianum spore was visually confirmed using a light microscope within 24 hours of plating on AWA. Individual spores were transferred to Potato Dextrose Agar with antibiotics [PDA+: 3.9% Potato Dextrose Agar (Difco) plus 0.5 mg/ml Streptomycin Sulfate and Penicillin G (Sigma) per 1 L deionized water] and grown at room temperature with ambient light. Up to thirty independent isolates from each collection (each originating from a different leaf) were randomly selected for DNA extraction and genotyping at 8 polymorphic microsatellite loci (GenBank accessions DQ272269—DQ272276) as previously described All seven sites were sampled in December 2002. Eight additional collections were taken from S1 between March 2002 and March 2004 . No collGenotypic diversity STF, theta θ, in FSTAT v 2.9.3.2 T. marchalianum were taken from different types of leaves  genetic differentiation by leaf type was also analyzed to ascertain if genotype–substrate specificities influence genetic structure in this species. Genetic structure was also evaluated using a Bayesian approach in Structure v2.3.2 Genetic differentiation was assessed using Weir and Cockerham's T. marchalianum isolates (18 from this study and 1 from Europe), 2 sister species (T. setigerum and T. maxilliforme), and 4 additional species from within the Order Helotiales  were determined from partial β-tubulin gene sequences. Uncorrected sequence divergence percentages between Tetracladium individuals were also generated. All Tetracladium sequences (GenBank accessions HQ123547—HQ123567) were obtained using primers BT1819R and BT2916 2-based and SH-like support values; values greater than 0.90 indicate significant support for a node.The phylogenetic relationships among 19 AI, where if AI of the data is significantly different from the results of 1000 randomized datasets (p≤0.05) the null hypothesis of recombination is rejected. The value of AI is known to increase with the number of loci tested; therefore, multilocus linkage disequilibrium was also measured using the locus number independent measure Dr, AI and Dr were calculated in MultiLocus 1.1.3b Multilocus linkage disequilibrium was evaluated using the Index of Association, Tetracladium marchalianum was sampled over both spatial  and temporal (S1: Mar02–Mar04) scales. Note that the December 2002 collection from S1 is included in both spatial and temporal spatial comparisons and is referred to as S1 and Dec02 accordingly. The 16 collections undertaken for this study  and smallest in summer (0.03–0.20)  (The fraction of leaves colonized by 03–0.20) . From Ma = 0.44) .Multilocus genotypes were obtained for 211 individuals from S1, collected between March 2002 and 2004, and 180 individuals from the 6 additional sites in Illinois and Wisconsin. In some cases fewer than 30 individuals per collection were genotyped due to the exclusion of samples that yielded low DNA concentrations and/or the isolation of fewer than 30 independent isolates . We obseGenotypic diversity was very high in all collections  for both spatial and temporal data and both models tested. Both groups identified by Structure (Group 1 and Group 2) were represented in every collection, but in different frequencies . Among cβ-tubulin sequence data, isolates identified as T. marchalianum based on morphology represent a single species , and is an order of magnitude lower than that observed between T. marchalianum and its sister species T. setigerum (4.88%) and T. maxilliforme (5.52%). These data do not support the existence of cryptic species within T. marchalianum.Based on Maximum Likelihood analysis of partial  species ; there iT. marchalianum appears to be primarily or effectively asexual ; the null hypothesis of recombination was rejected for all collections by the Index of Association  in the Sangamon River, Illinois, allowing us to simultaneously track changes in the fraction of leaves colonized by this species, and the frequencies of microsatellite alleles and multilocus genotypes. The fraction of leaves colonized by T. marchalianum increased and decreased seasonally as expected  illustrates that the population is remarkably stable over longer time scales. The outstanding question then is how can this genetic stability be achieved?From these data, it is clear that allele frequencies in populations of −2 , which includes leaf litter, in North American headwater streams is about 20g C mThe colonization of alternative substrates on land and in water might also foster the maintenance of genetic diversity and stabilize allele frequencies over time T. marchalianum. Thus, dispersal by alternative modes must occur. Neither the importance of alternative modes of dispersal, nor the frequency of gene flow between populations of aquatic hyphomycetes is currently known. Although unidirectional dispersal and gene flow can generate hierarchical population structure within and between rivers T. marchalianum from streams in neighboring and distant watersheds can be interpreted as evidence of overland gene flow  rDNA Although we did not observe genetic differentiation between populations of ulations . Unlike ulations . Note thnce data ; thus, tT. marchalianum and other aquatic hyphomycetes.The results of this study, in combination with the work of Laitung and colleagues AI suggest that T. marchalianum is effectively asexual . Furthermore, large population sizes (inferred from sampling success), lack of population differentiation over time or among sites, and evidence that cryptic species were not sampled in this study make related explanations for linkage disequilibrium in T. marchalianum unlikely. However, it remains difficult to decide whether high genotypic diversity is sufficient evidence to override the weight of the AI results presented here which indicate that T. marchalianum is effectively asexual in these populations.Analyses of reproductive mode, such as those performed in this research, commonly indicate that species do recombine in nature T. marchalianum reproduces sexually in only some populations; different modes of reproduction can dominate different parts of a species range There is a growing trend to use the distributions, presence/absence, and sequence variation of mating types and mating type alleles as evidence in determining reproductive mode T. marchalianum populations. The work presented here clearly shows that T. marchalianum populations can maintain high genotypic diversity and stable genetic structure regardless of annual boom-bust cycles. Both temporal stability and high genotypic diversity could indicate that populations of this fungus are very large, ensuring yearly population recovery and the maintenance of many genetically different lineages over time. Alternative habitats such as the roots and aerial leaves of living plants and woody debris in streams are also potentially important for the maintenance of diversity in populations of T. marchalianum and should be investigated further. Spatially hierarchical sampling of T. marchalianum populations revealed a lack of genetic differentiation at all but the largest scale (streams separated by ∼450 km), suggesting that overland dispersal and gene flow may be important drivers of diversity and genetic structure in this fungus, homogenizing populations within and between streams regionally. Thus, although the passive downstream dispersal of spores is the primary mode of aquatic hyphomycete dispersal, alternative modes of dispersal and their frequencies require attention. High genotypic diversity could indicate that T. marchalianum reproduces sexually in nature; however, genotypic diversity can be influenced by factors other than mode of reproduction. Thus it is not clear that high genotypic diversity should outweigh the results of analyses based on linkage disequilibrium, which indicate that this species is effectively asexual. We suggest that additional studies, including determination of the presence/absence, distribution, and sequence variation of mating types and mating type alleles, be undertaken before firm conclusions about the reproductive mode(s) employed by T. marchalianum are made.Seasonal fluctuations in the abundance of autumn-shed leaves and the corresponding changes in aquatic hyphomycete abundance are doubtlessly ecologically important for energy flow and nutrient spiraling in streams. However, these dynamics do not appear to influence the genetic make-up of Figure S1The asexually produced spore of T. marchalianum. Three branches radiate from the central axis, one originates from the side of a shorter branch (right). Both this short branch and the central axis terminate in globose cells. This photograph was taken using Nomarski optics, measure bar = 10µm.(TIF)Click here for additional data file.Figure S2The relative proportions of leaf types sampled in each collection  and colonized by T. marchalianum . Leaf data is not available for the Oct03 collection. In Aug 03 only three isolates were obtained.(TIF)Click here for additional data file.Table S1(PDF)Click here for additional data file.Table S2(PDF)Click here for additional data file.Table S3(PDF)Click here for additional data file.Table S4(PDF)Click here for additional data file.


Schistosoma japonicum parasites is increasingly difficult. This study examines the distribution of schistosomiasis in eastern China, taking advantage of the fact that the single intermediate host serves as a major transmission bottleneck. Epidemiological, population-genetic and high-resolution ecological data are combined to construct a predictive model capable of estimating the probability that schistosomiasis occurs in a target area . Results show that intermediate host genetic parameters are correlated with the distribution of endemic disease areas, and that five explanatory variables—altitude, minimum temperature, annual precipitation, genetic distance, and haplotype diversity—discriminate between endemic and non-endemic zones. Model predictions are correlated with human infection rates observed at the county level. Visualization of the model indicates that the highest risks of disease occur in the Dongting and Poyang lake regions, as expected, as well as in some floodplain areas of the Yangtze River. High risk areas are interconnected, suggesting the complex hydrological interplay of Dongting and Poyang lakes with the Yangtze River may be important for maintaining schistosomiasis in eastern China. Results demonstrate the value of genetic parameters for risk modeling, and particularly for reducing model prediction error. The findings have important consequences both for understanding the determinants of the current distribution of S. japonicum infections, and for designing future schistosomiasis surveillance and control strategies. The results also highlight how genetic information on taxa that constitute bottlenecks to disease transmission can be of value for risk modeling.Schistosomiasis japonica is a major parasitic disease threatening millions of people in China. Though overall prevalence was greatly reduced during the second half of the past century, continued persistence in some areas and cases of re-emergence in others remain major concerns. As many regions in China are approaching disease elimination, obtaining quantitative data on  Schistosomiasis is considered the second most devastating parasitic disease after malaria. In China, it is transmitted to humans, cattle and other vertebrate hosts by a single intermediate snail host. It has long been suggested that the close co-evolutionary relationship between parasite and intermediate host makes the snail a major transmission bottleneck in the disease life cycle. Here, we use a novel approach to model the disease distribution in eastern China based on a combination of epidemiological, ecological, and genetic information. We found four major high risk areas for schistosomiasis occurrence in the large lakes and flood plain regions of the Yangtze River. These regions are interconnected, suggesting that the disease may be maintained in eastern China in part through the annual flooding of the Yangtze River, which drives snail transport and admixture of genotypes. The novel approach undertaken yielded improved prediction of schistosomiasis disease distribution in eastern China. Thus, it may also be of value for the predictive modeling of other host- or vector-borne diseases. Schistosomiasis japonica is a major parasitic disease threatening 50–65 million people living in subtropical areas of China For instance, a basic epidemiological concept used in China's schistosomiasis surveillance and control strategy is ‘endemic area’. It refers to a region where a particular disease is prevalent Another instance where an evolutionary perspective could be useful relates to the fact that China's current control strategy primarily targets only two main hosts, humans and cattle Schistosoma japonicum is carried by a single species of snail with a peculiar amphibious life style, and there are no known cases of snail host-switching and/or host-addition in China Oncomelania hupensis a crucial target for disease control. Empirical evidence even suggests a close link between snail genetic characters and rates of infection in snails Unlike other snail-schistosome models, S. japonicum, and the fact that quantitative parasite data are increasingly difficult to obtain due to low rates of natural infections Given the large number of definitive hosts for We test whether genetic intermediate host characters , in principle, are reflected by previously defined endemic areas.We use a candidate set of topographical, ecological and genetic variables together with maximum entropy modeling to identify those explanatory characters that significantly discriminate between endemic/non-endemic areas.Based on the parameters with the highest discriminatory power, we then develop a SESR model, converting categorical infection data for administrative units into spatially explicit, high-resolution, and quantitative reaction data.Finally we compare the model data with actual human infection rates to evaluate our SESR model and to assess the significance of intermediate host traits for future epidemiological modeling of schistosomiasis.S. japonicum  is transmitted in the Yangtze River area by two snail subspecies, Oncomelania hupensis hupensis Gredler, 1881 and O. h. robertsoni Bartsch, 1946. The two taxa (and therefore the disease as well) have disjunct ranges; the eastern subspecies O. h. hupensis occurs in the lowlands of the Yangtze River below the Three Gorges  in six Chinese provinces. Specimens were largely collected between 1996 and 2005  in and around the lower Yangtze River basin. Thirty one of these sites are located within and fourteen outside of previously proposed endemic areas (This study includes 530 specimens of the eastern subspecies ic areas .All specimens were obtained before the completion of the Three Gorges Dam (TGD) project in 2007. It is assumed that dam-associated changes in water regime and sedimentation rates of the lower Yangtze River will have significant effects on the distribution patterns of the intermediate host and thus disease distribution c oxidase subunit I (COI) gene with a target length of 658 base pairs (excluding primer sequence). Forward and reverse primers for PCR amplification and DNA sequencing were LCO1490 Genomic DNA was extracted from individual snail specimens utilizing a CTAB protocol In order to test whether genetic snail parameters, in principle, can be explained by the distribution of previously defined endemic areas , we used two independent approaches. With an Analysis of Molecular Variance (Amova) As our snail populations were collected over a period of approximately 20 years . As previously suggested TN  and haplotype divergence (HMH) in order to assess within and between population differentiations, respectively. HD was estimated in DnaSP v5 based on equation 8.4 in Nei MH, we treated haplotypes as species TN and HMH were obtained by estimating the average pairwise distances between the population of concern and all other populations in the dataset.For identifying the genetic parameters that significantly reflect the spatial distribution of endemic areas, four candidate population indices were calculated from the COI dataset. They comprised within-site (‘diversity’) and between-site (‘divergence’) parameters. The first set of parameters consisted of nucleotide diversity π  and haplotype divergence (HMH) ranged from 0.0 to 1.0 and 0.801 to 1.0, respectively. Nucleotide diversity (π) and Tajima-Nei-distance (DTN) varied from 0.0 to 0.016 and from 0.0 to 0.019, respectively. Spatially explicit heat maps of genetic values for indices that were later used for the SESR modeling  are given in Supporting Genetic indices for individual populations are provided in Supporting CTΦ value for individuals collected during different sampling periods  was −0.016 (p = 0.464). As negative values should be interpreted as zero p-value indicates that our hypothesis  is not rejected, a possible bias in our SESR modeling caused by different sampling periods appears to be unlikely.The CTΦ = 0.18, p<0.001), the variation among populations within groups explained 40.4% , and the variation within populations 41.6% . As the CTΦ value is relatively high and as there is a significant partitioning of variance of snail parameters from populations belonging to endemic areas vs. extralimital populations, genetic characters appear to explain in part the endemic area state .The variation among groups of populations collected in endemic vs. non-endemic areas (Amova B) explained 18.1% of the total variation .TN, and HD was selected by MaxEnt  of this model had a median value of 0.97 (95% confidence limit: 0.80–1.00). When considering the two genetic and the three topographical/environmental parameters alone, the goodness-of-fit was 0.84 (0.57–1.00) and 0.92 (0.68–1.00), respectively . The aveThe visualization of the SESR model , conducted with the R-package dismo, is shown in I test. The Anova conducted showed that linear regression including spatial eigenvectors significantly explained more variation than without . Removing spatial autocorrelation from the infection dataset resulted in a final adjusted r2 of 0.338 . Thus, SESR model predictions are correlated with human infection rates.Our spatial risk model was compared to human infection rates TN, and HD) to discriminate between endemic/non-endemic areas ; (iii) the visualization of our SESR model indicated the highest risks for the regions of Dongting and Poyang lakes as well as some floodplains of the Yangtze River in Hubei and Anhui provinces ; and (iv) our model predictions are correlated with human infection rates . These findings are discussed further below in relation to the spatial distribution of endemic areas, the quality of predictive intermediate host traits (particularly genetic traits), and the implications of our risk modeling for future schistosomiasis surveillance and control strategies.The basic findings of our study were: (i) intermediate host genetic parameters inferred from the COI gene are correlated with the distribution of previously defined endemic areas ; (ii) the maximum entropy modeling suggested five explanatory variables  and are thus not associated with administrative entities. We suggest the complex hydrological interplay of lakes Dongting and Poyang with upstream and downstream sections of the Yangtze River in space and time as an important driver for the maintenance of the disease in eastern China.S. japonicumD). In addition to this diversity parameter, the model also suggested a divergence index (DTN) as risk variable, confirming the assumption that for understanding endemic areas and the potential isolation thereof, both genetic diversity  and divergence parameter  are of interest. In fact, one of the key findings of this study is that low divergence values indicate high risks , and annual precipitation (bio12)—are all environmental . These and several related variables have been previously suggested to be risk indicative Overall, the relative contribution of the genetic parameters is very similar to the predictive power of the environmental ones. However, given the already high values of environmental and genetic parameters alone, complementing genetic with ecological variables and vice versa resulted in a significant but only slight increase in total goodness-of-fit. Nonetheless, genetic parameters appear to be particularly important as predictive variables as their application considerably reduces the confidence intervals of the predictions compared to environmental variables alone . The results also demonstrate the value of these parameters for risk modeling and for improving the local accuracy of model predictions. Both divergence and diversity parameters are of interest, and genetic and environmental parameters can be complementary in such an analysis.Our findings have several implications for schistosomiasis surveillance and control in China, particularly under the current situation of decreasing overall disease transmission rates and increasing potential for re-emergence:With quantitative parasite data being extremely difficult to obtain, data of the co-evolved intermediate host can be used for risk modeling as previously suggested Given that risk values can vary over small distances, fine-scale units should be used in place of administrative boundaries when making surveillance and control decisions.High-risk areas as derived from risk models should receive surveillance and control priority, yet given the complex hydrological interplay of water bodies in the Yangtze River floodplains, information from extralimital areas should be considered as well for local strategies.As population genetic parameters of the intermediate host significantly increase the quality of risk predictions, future routine genetic surveys of snail populations are encouraged, particularly within and near high risk areas to better understand both local population structures as well as regional patterns of population isolation and exchange among lakes and rivers.In summary, this study stressed the role of intermediate host traits for understanding schistosomiasis occurrence in China in a spatially-explicit manner. It also showed that these traits may serve as sensible proxies for infection risks and highlighted the potential of genetic characters for future risk modeling.This study extended the traditional ecological niche-modeling approach, which is frequently used to predict the occurrence of parasite and/or host species, to an approach for predicting the probability of disease occurrence. This was possible by two key modifications/additions. First, we included epidemiological data  that were used to discriminate against. In a recent study on the West Nile virus mosquito vector O. hupensis in mainland China Whereas our findings could have important consequences for future schistosomiasis surveillance and control strategies, such study with pilot character also has some limitations. Given the considerable genetic diversity of Therefore, we encourage future deep-sequencing based genomic and transcriptomic studies of the intermediate host that aim at identifying the genes responsible for susceptibility and/or resistance to infections . Furthermore, the view point of the intermediate host system as major transmission bottleneck has proven to be useful for understanding disease distribution. However, more specific evolutionary analyses such as identifying loci under selection (‘selective sweeps’) in high risk snail populations would help to better delineate co-evolutionary processes leading to rapid adaptation Figure S1Spatially explicit heat maps of genetic indices used for the SESR modeling. Left: Tajima-Nei-distance (DTN); right: haplotype diversity (HD).(TIF)Click here for additional data file.Table S1Locality information, number of specimens per site, and GenBank accession numbers for the total of 530 specimens of Oncomelania h. hupensis studied. Populations located inside of endemic areas are marked with an asterisk.(DOCX)Click here for additional data file.Table S2Genetic population indices for 45 Oncomelania h. hupensis populations studied.(DOCX)Click here for additional data file.


Hemidactylus frenatus) was introduced to Moorea, French Polynesia in the remote eastern Pacific within the last two decades and accordingly is expected to exhibit low diversity. In contrast, we show that H. frenatus on Moorea has exceptionally high genetic diversity, similar to that near the native range in Asia and much higher than reported for other Pacific island reptiles. The high diversity in this recently founded population likely reflects extensive genetic admixture in source population(s) and a life history that promotes retention of diversity. These observations point to the importance of understanding range-wide dynamics of genetic admixture in highly invasive species.Invasive species often have reduced genetic diversity, but the opposite can be true if there have been multiple introductions and genetic admixture. Reduced diversity is most likely soon after establishment, in remote locations, when there is lower propagule pressure and with stepping-stone colonizations. The common house gecko ( Among the most important causes of native-species extinctions is interaction with invasive species Gehyra oceanica, Phelsuma laticauda), being recorded on the island for the first time in 2006 The islands of French Polynesia are among the most remote in the world and have a disharmonic biota derived by a mix of natural stepping-stone colonization from the western Pacific, natural colonizations from the east, and subsequent radiations, all overlain by recent human introductions Hemidactylus frenatus, is a prime example of an invasive species with demonstrated effects on native reptile faunas H. frenatus is uncertain. Recent studies have suggested a south Asian origin, although it may be that Indian and Sri Lankan lineages represent a different species from that in Myanmar and eastwards, as well as the invasive range of H. frenatusH. frenatus is now circumtropical, mostly as a human commensal, on both continents and islands. Invasion of the western tropical Pacific was assumed to have commenced some 4000 years ago with the early Polynesian and Melanesian migration, and continued with H. frenatus spreading as a stowaway on shipping and cargo boats th century, H. frenatus has spread through the eastern Pacific beginning in Hawaii in 1951, then Fiji in 1960 and Vanuatu in the 1970s–1980s H. frenatus is largely restricted to human structures H. frenatus was first reported in French Polynesia in 1989 H. frenatus has been on Moorea for at most 22 years (∼20 generations).The focus of the present study, the Common House Gecko H. frenatus on Moorea to have low genetic diversity, which could restrict their capacity to adapt to non-commensal environments. Alternatively, as preliminary analyses of this species on western Pacific islands revealed unexpectedly high mtDNA diversity H. frenatus on Moorea.Given the evident recent colonization, geographic remoteness, the likelihood of stepping-stone introductions, and also the low diversity observed in other species of lizards from the region, we expected H. frenatus has dispersed to occupy dwellings and other man-made structures in high numbers around the entire perimeter of the island; in contrast, no individuals of this highly conspicuous species were detected in repeated surveys of the interior forest  has highly diverse mtDNA, comparable to the variation observed across south-east Asia. There are 2 major groups  = 7.5). Samples from the Indonesian islands, closer to the presumed native mainland range, are represented in each of the two major mtDNA groups, and again are closely related to haplotypes from Myanmar (π = 0.043±0.022) is similar to that across all of Myanmar (θπ = 0.038±0.019) and greater than found in our geographically dispersed samples in Indonesia  between regions were relatively low, ranging from 0.39–1.24%  is on par with that across Myanmar (θπ = 0.022±0.01) and higher than that in Indonesia (0.014±0.007). Population differentiation at the nuclear level was significant (p<0.001) for every pair of populations by country .We obtained sequence data for three nuclear loci from nearly all individuals and, again, the Moorea population had high diversity ; Table 1 country . Howeveror K = 3 . As a prH. frenatus population on Moorea has high genetic diversity, similar to that across widely separated samples from in or near to the native range (Myanmar) and higher than that across Indonesia, a colonized area much closer to the mainland. The high mtDNA diversity of H. frenatus on Moorea contrasts strongly with low diversity observed in other lizards that have colonized the eastern Pacific islands Despite being geographically remote and having the potential for stepping-stone introductions, the Heteronotia binoei; average nDNA/mtDNA π = 0.11; H. frenatus  was also found in this study but more sampling of non-admixed populations within the native range are needed to understand this pattern.It has been hypothesized that geckos experience faster mitochondrial evolution H. frenatus can be explained by multiple introductions from geographically/genetically distinct native-range sources . To compare our major mtDNA lineages with those published previously, we chose a representative from each group and sequenced 12S rDNA  and sequenced using BigDye v3.1 (Applied Biosystems). The purified sequence product was sequenced on an ABI 3730 automated DNA sequencer. Sequences were aligned using Geneious Pro .We extracted DNA from vouchered specimens at the Museum of Vertebrate Zoology, University of California, Berkeley, and the California Academy of Science . Twenty 12S rDNA ; Fig. S212S rDNA . NuclearDxy and Da using the Tamura-Nei model; FST based on Tamura & Nei's model of sequence evolution π; Phylogenetic analyses for CO1 using maximum likelihood (ML) were conducted in RaxML H. brookii from Carrazan and Arnold Click here for additional data file.Figure S2Bayesian phylogeny of 12S sequences. Neighbor joining tree includes 12S sequences from this study, Bansal and Karanth (TIF)Click here for additional data file.Figure S3Unrooted Neighbor-joining network of (A) rpl18, (B) rpl14, and (C) LFABP.(TIF)Click here for additional data file.Figure S4tructure plots for (A) K = 2 and (B) K = 3.S(TIF)Click here for additional data file.Table S1Individual counts of Hemidactylus frenatus, Lepidodactylus lugubris, and Gehyra oceanica in 7 buildings in Moorea. Localities: 1) Gump Station; 2) Magasin Ami René; 3) Herman Perles in Hauru; 4) Magasin Lai-Assan in Haaptit; 5) Chez Teima in Maatea; 6) Electricite de Tahiti in Vaiare; 7) Magasin Lee Hen in Pao Pao.(DOC)Click here for additional data file.Table S2Diversity at each of the nuclear loci. Based on Tamura-Nei corrected average pairwise divergence ; within-population θπ ; population pairwise FST based on Tamura & Nei genetic differences . Bold numbers indicate statistically significant (p<0.05).(DOC)Click here for additional data file.Table S3Sequences sampled with associated Genbank accession numbers.(DOC)Click here for additional data file.


The three nestling personality traits form a behavioral syndrome on the phenotypic and genetic level. Overall, the phenotypic correlations provide a satisfactory description of the genetic ones, but significantly underestimate the magnitude of one of the pairwise genetic correlations, which mirrors the conclusion based on domestic and laboratory studies.When several personality traits covary, they form a behavioral syndrome. Understanding the evolutionary dynamics of a behavioral syndrome requires knowledge of its genetic underpinning. At present, our understanding of the genetic basis of behavioral syndromes is largely restricted to domestic and laboratory animals. Wild behavioral syndromes are mostly inferred on the basis of phenotypic correlations, and thus make the “phenotypic gambit” of assuming that these phenotypic correlations capture the underlying genetic correlations. On the basis of 3 years of reciprocal cross-fostering of 2896 nestlings of 271 families within a pedigreed population, we show that the nestling personality traits handling aggression, breathing rate, and docility are heritable ( Quantitative genetics is a statistical approach aiming to understand the genetic architecture of traits and their evolutionary dynamics . BehaviorP as the average of the weighted sum of genetic correlation rG and the environmental correlation rE, each weighted by the geometric mean of the fraction of variance, which is heritable (heritability) and not heritable, respectively. That is  conditions allow individuals to be both aggressive and bold. Clearly, under natural conditions, such environmental correlations may readily arise. The phenotypic gambit is then to assume that the phenotypic correlation neration . From a neration  and clearE. Indeed, based on published estimates of phenotypic and genetic correlations of behavior, Much of the literature on animal behavioral syndromes is based on phenotypic correlations, thereby explicitly or implicitly making the phenotypic gambit . For higty e.g., , of arouCyanistes caeruleus nestlings prior to fledging. We focus on nestlings, because this allows us to reciprocally cross-foster broods for 3 consecutive years within a pedigreed population. This design allows us to partition the phenotypic (co)variances into additive genetic, nest-of-origin, nest-of-rearing, and residual components. Because we work with nestlings, we are restricted to take simple field-based behavioral measures of how the nestling respond to the stress of being handled in terms of their docility during a fixed time period, their breathing rate . All birds included in this study bred in nest boxes, which were made available for breeding starting in 2003. Laying dates and clutch sizes were established by weekly checking all nest boxes. The clutchsize of blue tits in this population is typically 8–14 eggs. Hatching date (day 0) of at least the first chick in a brood was established by daily hatch checks carried out in the afternoon starting at 1 day prior to the day of expected hatching  regularly hatch 2 days after the first one. Our procedure of reciprocal cross-fostering was primarily designed to swap an equal number of nestlings of the same age and the same mass between two nests. Nests where the oldest nestlings were 2 days (day 2) were paired, primarily on the basis of the average mass of hatchlings encountered at day 0 and, when possible, with respect to their brood size. Our interest was in separating genetic from environmental effects, and differences in brood size between paired nests can from this viewpoint be considered part of the differences in the environment experienced by the nestlings. Controlling for discrepancies in body mass at cross-fostering between nestlings is important for this design, because even small discrepancies can readily translate into developmental differences later in life. An equal number of nestlings were reciprocally swapped between two nests. The pair of families between which nestlings were swapped, we here term “dyad,” and the brood in which a nestling hatched was termed “nest of origin” and the one in which it was reared “nest of rearing.” The decision on which nestlings were swapped was made random-systematically. In the first nest of a dyad, nestlings were weighed and individually marked by clipping a unique combination of their toe nails. By the toss of a coin, it was decided whether the heaviest nestling stayed in its nest of origin or was moved to another nest of rearing. Staying or moving was then alternated down the mass hierarchy in the nest. The same procedure was conducted in the second nest in a dyad if its brood size allowed doing so. In case of large differences in brood size between nests in a dyad, the nest with the smallest brood size was visited first and the number of nestlings swapped was thus approximately half the brood size of the nest with the smallest brood size. Nestlings of equal body mass were then selected from the second nest with the larger brood size (size-matched). In some cases, nestlings in nests with similar average mass of hatchlings had already diverged in their mass during the two pre-crossfoster days, in which case, cross-fostering was not carried out. Some families could not be part of the cross-fostering protocol, either because there was no other brood with nestlings of similar mass or because an odd number of nests hatched on the same day. Cross-fostering was carried out on first broods only. In 2007 and 2008, collection of data on behavioral traits overlapped with an experiment where the ectoparasite load of nests were reduced by taking the original nest cup from the nest box at cross-fostering (day 2) and replacing it by a nest cup that was cleaned of ectoparasites by microwaving see  and may On day 9, two to five feathers were sampled from the back of the nestling and stored in 95% ethanol. DNA was extracted from one small feather using the protocol of When the oldest nestlings were 16 days old, all nestlings were taken from the nest box and put individually in a numbered small paper bag . Once every nestling was placed in its own paper bag, all bags were reshuffled in order to randomize the order of further handling the nestling from the order in which nestlings were individually placed in the paper bags. Bags were picked one-by-one to carry out the following behavioral measurements.Docility: The nestling was immediately placed with its back on the palm of the observer's hand, held with its neck between the observer's index and middle finger, with one leg held by the foot between thumb and index finger and the other by the middle and ring finger. Once secured, the nestling was held still at approximately 40-cm from the observer's face. A stopwatch was started and the number of struggles during a fixed time period  were counted. Docility was expressed as −1 times the number of struggles per second, such that a higher docility score indicated an individual which was more docile . This test has analogs in production animals  tarsus length , which was measured twice, (2) head length from the tip of the beak to the back of the skull , (3) wing length (with a ruler), (4) tail length (with a ruler). Handling aggression is a Likert-scale score  relating the appropriate fixed effects to each individual, uA is a vector of additive genetic (random) effects, with ZA the design matrix relating the appropriate additive genetic effects to each individual. The summation ΣkZkuk allows for additional random effect structures on the individual and e is a vector of residual errors. G is defined as the matrix for vector uA and its elements (the genetic (co)variances) can be estimated by using information on the coefficient of coancestry Θij between individuals i and j, which is directly obtained from the pedigree. The additive genetic effects for trait t were assumed to be normally distributed with mean of zero  and with an additive genetic variance of σ2tA,. This variance  was estimated using Restricted Maximum Likelihood (REML) from the variance-covariance matrix of additive genetic effects, which is equal to Aσ2, where A has elements Aij = 2Θij. The models were implemented in ASReml 3  and solved under REML.where We used the social pedigree, where all offspring in one family are assumed to be full-sibs. The proportion of extra-pair nestlings in this population was not known, but population-specific data of nine populations suggests that between 7% and 25% of our blue tit nestlings could have been sired by an extra-pair male . BecauseWe initially considered each behavioral measure separately in order to test which fixed and random effects were important. The fixed effect structure of equation  accounteVariation after taking into account, the above stated fixed effects was partitioned into additive genetic, nest-of-origin, nest-of-rearing, and residual variance components. Estimation of the additive genetic (co)variances is based on the resemblance of population-wide relatives. In our design, the “nest of origin” variance refers to variance across broods of presumed full-sibs that occur in addition to the variance across broods based on their breeding value (additive genetic variance). Simulations show that cross-fostering within a pedigreed population is an approach which, when analyzed in an animal model context, allows to separate nest of origin from additive genetic variances . Nest-ofNest-of-rearing variation captures the common-environmental effect shared by all offspring in their nest of rearing during nestling ontogeny from day 2–16. This includes such varied aspects as variation in brood size across nests, differential parental  capacities in rearing the offspring under their care. Furthermore, environmental characteristics of the nest box of rearing  and local environmental conditions, such as food supply, will affect nest of rearing variance.F-tests with the residual degree of freedom numerically derived following We viewed the random effects for “nest of origin” and “nest of rearing” as variables related to our experimental design and therefore did not formally test their statistical significance. The additive genetic effect was tested by carrying out a Likelihood Ratio Test (LRT), where –two times the difference in log-likelihood between a model, including the additive genetic effect and a model without this effect was tested against a chi-square distribution with one degree of freedom. Fixed effects were tested using a Wald rA = 0).For the multivariate version of equation , we keptrA = rP). In general, there are several ways to compare phenotypic and additive genetic matrices and their various properties (We first derived the REML phenotypic correlations from the REML phenotypic (co)variance matrix, which can be obtained by summing up all the (co)variance components in the above described multivariate model or, equivalently, from a multivariate mixed model with the fixed effects and residuals. We term these here REML phenotypic correlations to clarify they are estimated under REML, but they are approximately equivalent to the Pearson correlation one would obtain after correcting the raw data for those fixed effects, we included in our model. The REML phenotypic correlation is based on all data and thus has much smaller uncertainty than the genetic correlations, which are based on comparing relatives and thus deal with a subset of all the data. A conservative test of the phenotypic gambit is thus to make a comparison of model likelihood when REML phenotypic correlations are interpreted as genetic ones. Formally, this was based on a LRT between the unconstrained multivariate model and one where the three genetic correlations were constrained to be equal to the REML phenotypic ones . In addition, we also tested using LRT whether each of the pairwise (trait–trait) genetic correlation was statistically equivalent to its REML phenotypic equivalent. Because one parameter is constrained, this test has one degree of freedom. We refer to LRT tests where genetic correlation(s) are constrained to be equal to the REML phenotypic one(s) as LRT . These nestlings originated from 271 broods, and were reared in 238 nests (for 33 broods one of the nests in the cross-foster dyad perished prior to assaying the nestlings). Average body mass of those nestlings, which were swapped between nests did not differ between the nests  and this difference was only a small fraction (1.6%) of the mean body mass of a 2-day old nestling (1.85 g). We therefore believe that our cross-foster protocol adequately avoided alteration of the composition of broods.The frequencies of handling aggression scores and of breathing rates showed clear unimodal distributions, but the distribution of docility was truncated, where the modal nestling was docile, with <2 struggles during the 10 sec assay period . HandlinAll the nestling behavioral measures had a significant heritable component . HeritabG matrix revealed significant negative correlations between handling aggression and breathing rate and between handling aggression and docility (P = 0.098) for genetically more docile individuals to also breath faster. The estimates of the correlations were similar for the different components , although the genetic correlations typically were stronger than the correlations for the other components : χ2 = 5.10, df = 3, P = 0.16) indicating that phenotypic correlations provided a reasonable description of the genetic correlations between the three behavioral traits. Nevertheless, pairwise testing suggested a statistical difference for the genetic and REML phenotypic correlation between aggression and docility (LRT (rA = rP): χ2 = 4.10, df = 1, P = 0.043), which was the strongest genetic correlation (rA = rP): χ2 = 2.04, df = 1, P = 0.15 and χ2 = 0.06, df = 1, P = 0.81, respectively).Constraining all three genetic correlations to have the same value as the REML phenotypic ones produced a nonsignificant change in likelihood (LRT (relation . The genP = 0.098) for a positive genetic correlation between breathing rate and docility. We interpret this finding as evidence that blue tit nestling genotypes differ in their sensitivity to stress. Under the stress of being outside the nest box (in isolation from their siblings) and of being handled, “easily-stressed” genotypes take fast, shallow breaths, and “freeze,” thereby becoming more docile and less aggressive in their response to being handled compared to less easily stressed genotypes.We performed behavioral assays on blue tit nestlings at a time when they have completed most of their morphological development. We focus on the quantification of three behavioral traits; aggression during handling, breathing rate, and docility, which are readily assayed under field conditions. Based on an experimental design where broods were reciprocally cross-fostered for 3 years and use of quantitative genetic methods, we find that these three traits have a modest, but clearly significant heritable component. This means that these behavioral measures indeed represent an intrinsic property of individuals and therefore describe an individual's personality and not merely reflect within-individual random variance. Additive genetic variance contributed approximately 16–29% of their phenotypic variance. The three personality traits correlate. We find that nestlings, which breathe fast and thus are presumably more stressed by the procedure  are moreWe can here only speculate about the mechanism by which the genetic correlation between our nestling personality traits could arise. We note, however, that our findings are consistent with all behaviors capturing pleiotropically acting variation in some physiological response to stress. For example, stress leads to hormone secretion by the adrenal system , which affect oxygen uptake (breathing rate) and behavioral responses . In partRepeating reciprocal cross-fostering during several years is a powerful method for describing additive genetic parameters . This ish2 = 17%), 10% of docility (h2 = 16%), and 14% of the variance in handling aggression (h2 = 28%). Although we here cannot establish the mechanism by which rearing-environmental effects are mediated, these effect sizes do demonstrate that ecological factors, including the social interactions between sibs, may have a considerable impact on a nestling's personality  aggressive/lower (higher) breathing rate/less (more) docile.Our design allows us to evaluate the validity of the phenotypic gambit by partitioning the phenotypic covariance matrix into its underlying components, including the additive genetic covariance matrix and other relevant components. There are many ways in which matrices can be compared , but we Research on animal personality is currently primarily based on phenotypic measures of personality traits and phenotypic correlations between these . While p