Macropus antilopinus; n = 78), and the common wallaroo . Both species are widespread across the tropical savannas of Australia except across the Carpentarian Barrier (CB) where there is a break in the distribution of M. antilopinus. We determined sequence variation in the hypervariable Domain I of the mitochondrial DNA control region and genotyped individuals at 12 polymorphic microsatellite loci to assess the historical and contemporary influence of the CB on these species. Surprisingly, we detected only limited differentiation between the disjunct Northern Territory and QueenslandM. antilopinus populations. In contrast, the continuously distributedM. robustus was highly divergent across the CB. Although unexpected, these contrasting responses appear related to minor differences in species biology. Our results suggest that vicariance may not explain well the phylogeographic patterns in Australia's dynamic monsoonal environments. This is because Quaternary environmental changes in this region have been complex, and diverse individual species’ biologies have resulted in less predictable and idiosyncratic responses.Tropical savannas cover 20–30% of the world's land surface and exhibit high levels of regional endemism, but the evolutionary histories of their biota remain poorly studied. The most extensive and unmodified tropical savannas occur in Northern Australia, and recent studies suggest this region supports high levels of previously undetected genetic diversity. To examine the importance of barriers to gene flow and the environmental history of Northern Australia in influencing patterns of diversity, we investigated the phylogeography of two closely related, large, vagile macropodid marsupials, the antilopine wallaroo ( Despite considerable progress in our understanding of global phylogeography, significant biases remain (Beheregaray 2), and has high levels of species diversity and endemism  and common wallaroo (Macropus robustus), two sympatric species of closely related, large, vagile, macropodid marsupials  species that span Northern Australia. The antilopine wallaroo  is a savanna woodland specialist grazer  a widespread generalist browser/grazer that is distributed throughout Australia, including across the CB, and is often associated with rocky areas  would show more pronounced genetic structuring across the CB than the widespread habitat generalist (M. robustus). Understanding the impact of the CB in a wide range of taxa will clarify how this vicariant barrier has influenced the current genetic structure of communities across Northern Australia and increase our knowledge of the biogeographical processes shaping patterns of diversity in this region.This study aimed to use molecular genetic analysis  of bothM. antilopinus andM. robustus encountered during field surveys throughout Northern Australia. Additional samples were obtained from wildlife parks, carers, managers, and fellow researchers. In total, samples were obtained from 78 M. antilopinus , 14 sympatricM. r. woodwardi from WA (n = 6) and NT (n = 8) and seven sympatricM. r. robustus from northeast Qld. For phylogenetic analyses, three additionalM. r. robustus from outside the zone of sympatry , two euros (Macropus r. erubescens) from WA, one Barrow Island euro (Macropus r. isabellensis), and one black-wallaroo (Macropus bernardus) were also obtained . Samples were preserved in 70–90% ethanol before genomic DNA (gDNA) was extracted using a “salting out” method  using marsupial-specific primers , Sydney. Mitochondrial sequences were aligned and edited using Sequencher v4.10.1 , with haplotypic and nucleotide diversities estimated in Arlequin v3.5.1.2 :Me14, Me15, Me16, Me17, Me28,T3.1, T15.1, T31.1, T32.1, andT46.5 (Taylor and Cooper Macropus giganteus):G16.1 andG26.4 , expected heterozygosity (HE), allelic diversity (A), and allelic diversity corrected for sample size (An) were estimated using Fstat v2.9.3  and unique (private) alleles (uA) per locus was also calculated. Differences in diversity indices amongst sampled populations were assessed via a Wilcoxon rank sign test .Conformance to Hardy–Weinberg equilibrium (HWE) for each microsatellite locus and linkage disequilibrium (LD) was tested via ure Rice . Observe3 Goudet . The meaST; microsatellitesFST) amongst geographic populations was estimated and tested for significance in Arlequin (mtDNA) and in Fstat (microsatellites) using 1000 permutations. For the mtDNA data, the presence of isolation-by-distance was tested using a Mantel test  present using values ofK between one and six. For eachK, 10 independent replicates were run for 200,000 iterations after a burn-in of 100,000 iterations. The inferred number of populations (K) within the sample was deduced using both maximum posterior probability  as the outgroup. For the ML analysis, a GTR+I+G model was estimated with PAUP* v4.0b10;  in RSwofford  using MoM. robustus andM. antilopinus across the CB using fossil calibrations in the program Beast version 1.7.4  to provide three fossil calibration points on an extended phylogeny . The three nodes were dated following Meredith et al.  with a maximum date of 15.97 MYA (associated with an absence in System C deposits from stratigraphic bounding (Riversleigh)) and a minimum date of 3.4 MYA; for node C, with a maximum of 11.61 MYA . Due to the gaps in the fossil record, a normal prior distribution was implemented to account for any bias in the fossil record. The analysis was run using a relaxed molecular clock, the Yule tree prior, and an uncorrelated lognormal prior and using the GTR+I+G model outlined above. Two independent MCMC chains were run for 10 million generations with sampling every 1000 generations, and a 10% burn-in was removed from the posterior samples. Tracer version 1.5  where Θ = 4 Neμ (Ne is the effective population size andμ is the mutation rate per site),M = m/μ (m is the immigration rate per generation).Macropus antilopinus and northernM. robustus were separated into populations on either side of the CB, and analyses were run separately for mtDNA and microsatellites using the Bayesian analysis approach  tree, and excluded missing data. Mutation rates were estimated from the data for microsatellite analyses but constant for the mtDNA analyses. For all analyses, two independent analyses were run, each run with one long chain and four heated chains, sampling every 1000 steps with 400,000 genealogies recorded after a burn-in of 100,000 steps.We tested for current gene flow (migration) across the CB using n Beerli . MigrateM. antilopinus and northernM. robustus from mtDNA sequences using Beast version 1.7.4. Analyses were run separately for NT/WA and Qld populations, using a strict clock, the HKY model with a proportion of invariant sites, and the γ distribution model based on Modeltest 3.06. A total of 10 million generations were run using five piecewise intervals  to estimate Ne and a mutation rate of 0.03824 based on the output from the fossil calibrations. Tracer version 1.5 was used to assess convergence based on effective sample sizes, and the BSP analysis was performed using the stepwise constant model.Bayesian skyline plots (BSPs) were also constructed to test for historical demographic expansion forCR haplotypes (648 base pairs) were identified withinM. antilopinus, 16 in Qld (n = 19) and 32 in NT/WA (n = 57). No haplotypes were shared between the regions (Table S2). WithinM. robustus, 20CR haplotypes were identified, 11 fromM. r. woodwardi (n = 14), six fromM. r. robustus (n = 6), two fromM. r. erubescens (n = 2), and one fromM. r. isabellensis. Haplotypic diversity was high (∼1.0) amongst the Qld and NT/WAM. antilopinus andM. robustus populations (Table S2), but nucleotide diversity (π) was substantially lower in the QldM. antilopinus population , and there was no consistent evidence of LD (P > 0.05). A total of 143 microsatellite alleles were identified inM. antilopinus (41% unique) and 158 withinM. robustus . Almost all identifiedM. antilopinus alleles 97% (138/143) were found within the NT/WA, while only 56% (80/143) were present in Qld. WithinM. antilopinus, few of the alleles present in Qld  were unique, while 46% (63/138) of alleles were unique to the NT/WA population (Table S3). Despite the small sample of northeast QldM. robustus, 95 alleles were present (17% unique), while 123 alleles were identified in NT/WA (25% unique)M. robustus (Table S3).All microsatellite loci were polymorphic in bothM. antilopinus andM. robustus with allelic diversity (A) ranging from 10.3 to 11.5 and expected heterozygosity (HE) from 0.73 to 0.81 (Table HE was similar in the QldM. antilopinus population (HE = 0.68), allelic diversity corrected for sample size (An) was significantly lower than all other sampled populations (P < 0.008). The QldM. antilopinus population also had values forA,rA, anduA that were significantly lower than both the NT/WAM. antilopinus (P < 0.02) andM. robustus (P < 0.003) populations .Levels of genetic diversity were high in NT/WAFST and ΦST) between all four populations was significant, but was substantially lower between the Qld and NT/WAM. antilopinus populations  than between the Qld and NT/WAM. robustus populations  andM. robustus , as well as within a combined Qld/NT/WAM. robustus population , but not the QldM. antilopinus  orM. robustus  populations, nor in a combined Qld/NT/WAM. antilopinus population .Genetic differentiation  or three (maximum ΔK) populations were present in the complete data set . These inferred populations corresponded toM. antilopinus andM. robustus whenK = 2, orM. antilopinus and twoM. robustus clusters whenK = 3  or three (L[K]) populations inferred , but with most individuals showing some admixture  in the far eastern NT  within the NT clade and 1.8% (ASD) within the NT/Qld/WA clade. Two major lineages were also resolved withinM. robustus . Within this second clade,M. r. woodwardiandM. r. robustus were paraphyletic, as wereM. r. erubescens andM. r. isabellensis. There was 7.8% ASD between the two majorM. robustus clades, 2.6% (ASD) within the northeast QldM. r. robustusclade and 4.5% (ASD) within the widespreadM. robustus clade. Amongst recognizedM. robustus subspecies, ASD ranged from 2.3% to 7.3% (mean 5.7%).The results of the MP and ML analysis are not presented separately but are summarized on the ree Fig. . The MP nus Fig. . Amongstses Fig. , the firses Fig. . There wtus Fig. , and thetus Fig. . One linBeast analysis spanned zero suggesting no strong evidence of autocorrelation of rates in the phylogeny. In addition, the coefficient of variance did not abut zero suggesting a relaxed molecular clock was appropriate rather than a strict clock for analysis. The twoM. antilopinus clades were estimated to have diverged 2.453 (1.085–4.279) MYA, while the twoM. robustus clades were estimated to have diverged 3.278 (1.641–5.372) MYA  KYA ; however, for Qld, there was no evidence of a change in population size in the last 40 KYA  with the potential for a slight increase in size for the Qld population ∼150 KYA  have identified two major patterns across the CB, strong differentiation . There was also evidence of gene flow across the CB with Migrate-N indicating high levels of gene flow both historically (mtDNA) and more recently  movement ofM. antilopinus from the NT across the CB onto CYP. The BSP showed a population expansion forM. antilopinus in the NT/WA about 200 KYA, while the Qld population appears constant from around 40 KYA. The 16 uniqueCR haplotypes found in Qld were restricted to a singleM. antilopinus clade  MYA  across the CB from NT/WA into Qld, than the reverse , during which the Carpentarian Plain was exposed providing a vast land connection between CYP and the Top End. However, there is no evidence of panmixia betweenM. antilopinus populations across the Carpentarian Plain at this time. This suggests that while paleoecology reconstruction from pollen cores indicates the Carpentarian Plain, when last exposed, supported a tropical savanna  and clustered separately in the Structure analysis  and 8.3  compared with northwestern (NT/WA)M. r. woodwardi. This level of divergence is greater than that amongst all currently recognizedM. robustus subspecies (mean 5.7%). These data raise some intriguing questions about the interrelationships and evolutionary history ofM. robustus populations throughout the species’ continental distribution  MYA, which predates most of the divergences reported for other taxa  to be less impacted by the CB than the disjunct habitat specialist (M. antilopinus). Nevertheless, it is intriguing that two closely related sympatric species should have such profoundly different responses to climatic cycling with evidence for both dispersal (M. antilopinus) and vicariance (M. robustus) across the CB. These radically different responses are challenging to explain but may result from subtle differences in species biology.M. robustus is more able to utilize steep and rocky habitats whileM. antilopinus prefers flat or gently undulating areas  were widely dispersed and sometimes co-occurred , northeast Arnhem Land, and Pungalina  possessed unique haplotypes that were closely related phylogenetically M. antilopinus samples (n = 4) also possessed four unique but related haplotypes , or theclades 6.% ASD werBiogeographic barriers are of interest to evolutionary biologists due to their expected influence on the historical evolutionary processes of many codistributed taxa. Although the CB is seen as a major vicariance barrier in Northern Australia, our data suggest that it can also act as a porous filter and that dispersal can play a significant role in shaping the distribution and genetic structure of Northern Australian species. This is consistent with evidence of disjunction with subsequent re-expansion for several species now continuously distributed in savanna habitats across this region (e.g., Toon et al. Our results also suggest that the Northern Hemisphere view that patterns of species diversity are largely shaped by periods of glaciation and vicariance events Hewitt , may not


Inhabitants of rural areas can be tempted to migrate to urban areas for the type and range of facilities available. Although urban inhabitants may benefit from greater access to human and social services, living in a big city can also bring disadvantages to some residents due to changes in social and physical environments.We analysed data from 4,208 women aged >15 years old participating in the fourth wave of the Indonesia Family Life Survey. Chronic condition risk factors – systolic and diastolic blood pressure (SBP and DBP), body mass index (BMI), and tobacco use – among women in four major cities in Indonesia  were compared against other cities. Fractional polynomial regression models were applied to examine the association between living in the major cities and SBP, DBP, BMI, and tobacco use. The models were also adjusted for age, education, employment status, migration status, ethnic groups, and religion. The patterns of SBP, DBP, and BMI were plotted and contrasted between groups of cities.p<0.005). Living in major cities increased the risk of having higher SBP, DBP, BMI and being a current smoker. Chronic disease risk factors in major cities were evident from younger ages.Chronic condition prevalence was higher for women in major cities than in contrasting cities (Women residing in Indonesia's major cities have a higher risk of developing chronic conditions, starting at younger ages. The findings highlight the challenges inherent in providing long-term healthcare with its associated cost within major Indonesian cities and the importance of chronic disease prevention programmes targeting women at an early age. Urbanisation creates opportunities for people to gain better access to employment and to human and social services. Whereas two decades ago fewer than 40% of the world's population lived in cities, today this figure is greater than 50% . The proUN-Habitat recorded that, globally, more than half the people who moved from rural to urban areas during 2008–2009 were women . Women aStudies show that urban populations have better health on average than non-urban populations , 6. HoweToday, chronic disease has replaced infectious disease as the main specific cause of death among women as a result of their physical and social environments . Many ciIn the last few decades, Indonesia has achieved significant improvement in overall health status. One of the indicators of this improvement is life expectancy. Over the last three decades, life expectancy has increased from 46 years 1971) to 70.5 years (2007) 71 to 70.. This im2 in 2010 , conducted in 2007/2008. The IFLS was first conducted in 1993 and has been repeated three times, in 1997/1998, 2000, and 2007/2008 (IFLS-4) . The samwww.rand.org/labor/FLS/IFLS/ifls4.html). Approval for the IFLS-4 project was granted by the institutional review boards at RAND and Gadjah Mada University, Yogyakarta, Indonesia. The use of the data set for this study was approved by the Human Research Ethics Committee, University of Newcastle, Australia.The IFLS-4 data are open for public use with prior registration on the study website  and diastolic blood pressure (DBP). BMI was calculated from weight and height measurements conducted at the same time as the blood pressure measurement. Current smoking status was based on participants’ response to questions related to tobacco use.Groups of cities. In this study, cities where the IFLS-4 was conducted were classified into two groups: major cities and contrasting cities. By this classification a major city is 1) the national or provincial capital city; 2) surrounded by satellite cities, representing its status as a centre of business and economic activities; and 3) heterogeneous in population. Following these criteria, we classified Jakarta, Surabaya, Medan, and Bandung as major cities. These are the four largest cities in Indonesia, with each described briefly below.Jakarta is the capital city of Indonesia and is the most populous city in Indonesia. As the capital city of Indonesia, DKI Jakarta has become the centre of national government, the economy, development, and education in the education in the country. Main economic sectors include services and trading, hotels and restaurants, and manufacturing .Surabaya, the capital city of East Java Province, is located on the northern coast of East Java. The inhabitants of Surabaya come from different ethnic groups in the country. They are Malay, Chinese, Arabic, Sundanese, Batak, Dayak, and Balinese, and the majority ethnic groups are native Surabaya and Maduranese . With vaMedan is the third largest city in Indonesia, after Jakarta and Surabaya. Of the four largest cities in the country, Medan is the only city located outside of Java Island. As the capital city of North Sumatra Province, Medan is the centre of provincial government administration, the economy, communication, tourism, and regional trading. Currently there are 86 national companies and 17 international companies operating in industrial locations in the city .Bandung is the capital city of West Java Province. Located not far from Jakarta, Bandung is a centre of education, industry, and tourism. It is where 437 tertiary education institutions, 24 shopping malls, and more than 9,000 stores are located . The citThe other cities in 20 provinces surveyed in the IFLS-4, which did not meet the criteria for major cities, were classified as contrasting cities. Cities included in this group were less populated and included cities that cover suburban or rural areas, cities surrounded by rural areas, or district capital cities. They include newly developed cities, cities with wider suburban areas compared to the major cities, and those with more homogenous backgrounds than the major cities.We included age, educational attainment , five quintiles of household wealth based on household assets, employment status (being in paid work vs not in paid work), in-country migration (migrant vs non-migrant), ethnic background (Javanese vs non-Javanese), and religion (Moslem vs non-Moslem) as predictor variables.t-tests were applied to contrast the mean of SBP, DBP, and BMI. We then applied fractional polynomial (FP) regression models to examine the association between living in major cities and hypertension, obesity, and tobacco use, adjusted with other predictors described above. Next, we plotted SBP, DBP, and BMI against the predicted line across age in both settings to further contrast the pattern of chronic disease risk factors across ages in major and contrasting cities.We compared the prevalence of chronic conditions and current smoking among women in major cities and contrasting cities. Weighted prevalence was calculated using the sampling weight factor constructed by RAND  to allowThe FP model was introduced by Royston and Altman in 1994 , 34. It p, which is chosen from the set {−2, −1, −0.5, 0, 0.5, 1, 2, 3} (p1 as FP1(p1)=α+β1Xp1 and the second-degree FP equation with power p1 and p2 as FP2=α+β1Xp1+β2Xp2. Hence, the general equation of FP is denoted as follows:m=3, and p is chosen from the set {−2, −1, −0.5, 0, 0.5, 1, 2, 3} , although the difference was not large.A total of 4,208 women aged 15 years and above were included in the analysis. Of these women, 1,400 were residing in the major cities, and the other 2,808 were residing in the contrasting cities. The mean age was 36.1 years old (SD=15.1). The mean age in major cities was significantly younger than in contrasting cities , with the prevalence of current smoking almost doubled in the major cities (p<0.05).p<0.001). In general, hypertension, uric acid/gout, and arthritis/rheumatism were the three most prevalent diagnoses among women aged 40 years old in the cities. The prevalence of hypertension, diabetes, lung conditions other than asthma, and uric acid were significantly higher in the major cities than in the contrasting cities (p<0.05).As shown in The association between living in a major city and chronic disease risk factors are shown in As shown in Living in major cities also increased the odds of being a current smoker. Other predictors associated positively with being a current smoker included being a younger woman, being in the lower quintiles of household wealth, and having non-Javanese background.The predicted SBP, DBP and BMI for women in major cities were higher across age groups than those living in contrasting cities . Pre-hypThis study found that the prevalence of chronic conditions among women in major cities was significantly higher than those in contrasting cities (17.5% vs 12%), even though the mean age in the major cities was younger than in contrasting cities (35.1 years old vs 36.6 years old). Among women age 40 years and above living in the cities, the prevalence of chronic disease in major cities was significantly higher than in contrasting cities. Hypertension was indicated as the most prevalent chronic condition, followed by degenerative joint disease, such as gout and arthritis, and lifestyle-related chronic disease, such as diabetes and heart problems. In Indonesia, hypertension is the most frequently diagnosed condition by health centre among the elderly population, and urban life has the potential to increase the risk of having high blood pressure . AnotherAfter adjustment for other predictors, the study found that women who were residing in major cities had a higher probability of high blood pressure (SBP and DBP), higher BMI – which in turn resulted in a higher probability of being overweight – and being a current smoker. Knowing that these three conditions are major risk factors for chronic disease, there is a greater chance that women in major cities have a higher probability of having chronic disease as well. Access to healthy food, rapid shifts in income, and changes in occupation types among the inhabitants of big cities could lead to a shift in lifestyle towards less physical activity and unhealthy dietary habits , 38.In addition, as suggested by Levine , the higWith the higher odds of having hypertension, becoming overweight, and being a current smoker for women in the major cities compared to the contrasting cities, there is an indication of an increasing burden of chronic disease in the major cities over time. Hence, the difference in chronic disease prevalence between major and contrasting cities will be widened further. These findings highlight the importance of chronic disease risk factor screening at an early stage. It is particularly important given that chronic disease has now become the highest burden of disease among urban residents, with particular relevance to urban women.Compared to contrasting cities, the ages of women having pre-hypertension in major cities – SBP between 120 and 130 mmHg or DBP between 80 and 90 mmHg  – were fSocio-economic status, reflected by educational attainment and household wealth, was another factor found to be a significant predictor for high blood pressure, having higher BMI, and being a current smoker. Women in the lower economic group had a higher risk of having hypertension and becoming a smoker. On the contrary, women with higher economic status had a higher risk of being overweight – reflected by higher BMI. It was also shown that well-educated women had a lower risk of being overweight. These findings supported another cross-sectional study among urban dwellers in China, where women with a lower level of education and higher level of income had a higher risk of being obese . With reThis study involves a cross-sectional analysis that provides comparisons between women in different groups of cities, but with limitations in describing the exact patterns of urban living (including the period of living in the cities) and the development of disease. Despite its limitations, the study provides evidence on the emergence of non-communicable disease among women in Indonesia's cities, emphasising the importance of providing a women's health programme that extends beyond reproductive health.This study has shown that even though major cities may offer more access to social and human services compared to other settings, women residing in Indonesia's major cities do not have better chronic condition health outcomes than those who live in the contrasting cities. Better access to healthcare among women in major cities could provide opportunities for better Non-communicable Disease (NCD) control, with a gender-responsive NCD control programme at the primary level. Additionally, in order to expand the NCD control programme among women, integration of the NCD programme with other established primary healthcare programmes, such as family planning and maternal health, is worth considering.With a higher probability for having hypertension and obesity and being a current smoker in the major cities – starting from an early age – we can expect a higher burden of chronic conditions among this population group, into the future. Our findings highlight the challenge of providing long-term healthcare, with its associated financial cost, in the major cities of Indonesia. The ageing population and the importance of a robust chronic disease prevention programme will make this an imperative. Therefore, in an era of decentralised health systems, it will be necessary for local governments to emphasise both healthcare provision and health promotion – targeting women at an early age – to prevent non-sustainable health costs into the future and incorporating ageing health into the current public health agenda.


Prunus africana in North-western Ethiopia. This is a floristically impoverished region where all but a few remnant forest patches have been destroyed, mostly by anthropogenic means.Habitat loss and fragmentation may have detrimental impacts on genetic diversity, population structure and overall viability of tropical trees. The response of tropical trees to fragmentation processes may, however, be species, cohort or region-specific. Here we test the hypothesis that forest fragmentation is associated with lower genetic variability and higher genetic differentiation in adult and seedling populations of FST comparisons showed evidence for significant population genetic differentiation. Mean FST values were significantly greater in seedlings than adults, even after correction for within population diversity, but varied little with patch size or isolation.Genetic diversity  was significantly greater in large and less-isolated forest patches as well as in adults than seedlings. Nearly all pairwise Analysis of long-lived adult trees suggests the formerly contiguous forest in North-western Ethiopia probably exhibited strong spatial patterns of genetic structure. This means that protecting a range of patches including small and isolated ones is needed to conserve the extant genetic resources of the valuable forests in this region. However, given the high livelihood dependence of the local community and the high impact of foreign investors on forest resources of this region, in situ conservation efforts alone may not be helpful. Therefore, these efforts should be supported with ex situ gene conservation actions. Persistent dependence of human livelihoods on agriculture, tropical rainforests and products has remained the major cause of overharvesting, deforestation, forest degradation and land use alterations in most developing tropical regions ,2. Such Conservation genetics studies are expected to provide information that enables a better understanding of the ways in which human disturbance and fragmentation processes affect population viability in fragmented landscapes and this may assist in devising more appropriate management options ,13. SmalHowever, empirical research has documented contradictory genetic effects of habitat loss and fragmentation especially on long-lived tree species . That isNevertheless, genetic effects of fragmentation may still remain undetected or absent depending on a number of other contexts. First, capacity of some tree species for extensive gene flow by pollen or seed can buffer a population against genetic effects of fragmentation ,30. SecoPrunus africana, Albizia schimperiana, Albizia gumifera and Celtis africana. According to the recent natural vegetation map of Eastern Africa [Prunus africana and Albizia schimperiana. The remnant forest patches are under heavy pressure due to uncontrolled grazing and illegal logging by the local inhabitants. The remnant forest patches are thus highly threatened due to human disturbance and progressive subdivision. Conducting empirical investigations on the genetic effects of human disturbance and habitat fragmentation on representative taxa may help to formulate valid recommendations for the conservation and restoration of these fragile ecosystems.The remnant forest patches of Awi Zone in North-western Ethiopia are mainly dominated by n Africa , nearly Prunus africana (Hook.f.) Kalkman (Rosaceae) as a model species to assess the impacts of habitat loss and fragmentation in Awi Zone of North-western Ethiopia. P. africana is a long-lived monoecious tree species that has been listed as Endangered on Appendix II of CITES since 1995 and as Vulnerable in the IUCN Red List of Threatened Species since 1998 [P. africana is highly valued in the international medicinal plant trade as it is the major source of an extract used to treat urinary disorders, prostate gland hypertrophy and benign prostatic hyperplasia [P. africana at the Kagamega forest revealed significantly lower genetic variability among seedlings than adults [In this study, we chose nce 1998  but is lerplasia . Becauseerplasia . In Kenyn adults . Howevern adults , are worWe used seven polymorphic microsatellite loci to test the hypothesis that recent and ongoing forest fragmentation explains patterns of genetic variation in remnant forest patches. Under this scenario, the relative magnitude of genetic differentiation among remnant patches would be associated with their degree of fragmentation. Accordingly, we predict a) less variation in smaller relative to larger forest patches; b) more evidence of bottleneck effects in smaller than larger patches; and c) more structure in seedlings than adults. Alternatively, if recent and ongoing fragmentation does not explain patterns of genetic variation in remnant patches, we predict either panmixia across the study area, or an isolation by distance (IBD) effect associated with historical structuring of the forest that predates its fragmentation.Analysis of data in Micro-checker revealed the potential occurrence of null alleles in one of the 8 loci. We analyzed data at both 8 and 7 loci and found some variation in statistical outputs between the two. As a result, we decided to omit the locus suspected to contain null alleles (EMPAS01) from further analyses. No significant linkage disequilibrium was detected for the various locus pair combinations consistently across all populations indicating independence of loci used. Significant Hardy-Weinberg disequilibria were detected for some locus-population pairs for locus EMPA001 and PS12A02 even after sequential Bonferroni corrections. However, these loci did not consistently deviate from Hardy-Weinberg equilibrium for all populations. We retained these loci for analyses because results were robust regardless of their exclusion or inclusion.S) per patch across 7 loci was significantly greater in adults (HS = 0.77) than seedlings (HS = 0.71). Similarly, the overall mean of allelic richness per patch was significantly greater in adults (5.96) than seedlings (4.85)  in adults and 5 to 9.1  in seedlings. The difference between these overall means was significantly different from zero (Table P = 0.0201) greater for large  than small  forest patch groups (Table P = 0.0425) greater for less-isolated  than isolated  forest patches  greater for seedlings (FST = 0.075) than adults (FST = 0.035)  or patch isolation . Even so, linear regression, though non-significant, revealed a positive relationship between FST and patch isolation and an inverse relationship with patch size  for the combined data set demonstrating an isolation by distance (IBD) effect  and seedlings  (Table All pairwise ) Figure , but vart Figure . The ManFST = 0.05  and nearest patch distance (edge to edge) consistently in the combined data set, adults and seedlings  providing evidence for spatial patterns of genetic structuring and potential causes after controlling for correlating factors; 2) showing no evidence for recent reduction in effective population sizes; and 3) accounting for effects of genetic diversity when evaluating genetic differentiation.In addition to the findings of Farwig et al. , our resDespite the relatively larger sample size of seedlings, we found a significantly lower number of alleles, expected heterozygosity and allelic richness in the seedling than adult populations. The larger homozygosity among seedlings appears to be a result of mating amongst close relatives  as is common in trees, but this may decay via demographic thinning or selection against homozygotes. In addition, the sampling was performed on sets of seedlings gathered around mother trees. Therefore seedlings are likely related and share some alleles, thus reducing allelic richness and heterozygosity. Also, gene flow might have changed due to the changed spatial distribution of adult trees following human disturbance and fragmentation -41. ThusP. africana trees in the remnant forest patches  in the past, 2 Ne – 4 Ne generations. Given the ongoing severe deforestation and fragmentation processes, we might have expected to detect signs of bottlenecks in the remnant populations, especially the more isolated ones. This finding, however, may be additional evidence for the very recent human disturbance and that fragmentation ‘may not yet have left a population genetic signature’ [There was no sign of severe reductions in effective population sizes  dispersal of P. africana in NW Ethiopia. The limitation in propagule dispersal was supported by our first-generation migrant detection analysis in Geneclass2 [P. africana are facilitated by animal dispersal vectors, possible disturbance and fragmentation effects on mobility, abundance and survival of these agents might have caused the limitation in pollen and seed dispersal [Pairwise neclass2 , which dispersal . In partispersal  and seleispersal . In addiispersal ,38.FST values did not significantly vary with patch size or isolation but observed patterns matched theoretical expectations perhaps implying slightly better gene flow rates during early or pre-fragmentation stages but reduced gene flow after severe fragmentation. These results could also be attributed to the lack of statistical power due to the relatively small sample sizes of patch isolation and patch size groups or the limited number of marker loci, or may indicate little or very recent impacts of fragmentation on population differentiation of P. africana in NW Ethiopia.Average population-specific FST values of the seedling cohort were significantly greater than the adult. One explanation could be that the seedling cohort was more genetically isolated than the adult as a result of recent human disturbance and habitat fragmentation. However, it may also reflect the differences in genetic diversity between the two cohorts. As FST measures within versus between population diversity, an increase in within population diversity will automatically reduce the FST value. The use of standardized FST (F’ST) and newer estimates of genetic distance, Jost’s Dest[The mean population-specific st’s Dest,56, may P. africana are thought to be highly mobile animals such as frugivorous birds and mammals [in review), however, demonstrated a significant impact of fragmentation on the diversity and abundance of avian frugivores. Such impacts of fragmentation on potential dispersers could limit propagule dispersal [FST[P. africana and are hoping to communicate this in a separate paper.We would have expected a non isolation by distance effect, with only the fragmentation variables affecting the spatial patterns of genetic differentiation among the populations. However, both simple and partial Mantel tests revealed a strong effect of distance on genetic differentiation particularly in the adult populations. This pattern almost certainly predated forest fragmentation in this region, thus indicating limitation in both pollen and seed dispersal even in the previously vast and contiguous forests of the study region. Our first-generation migrant detection analysis for the adult populations also supports this prediction. The migrant analysis revealed that less than 8% of sampled adult individuals from a patch were migrants. As the main seed dispersal agents of  mammals , better ispersal  and hencispersal ,59 and irsal [FST. We are P. africana are partially recalcitrant, which means that seeds may not be stored in gene banks for longer periods. Field gene banks appear better options when considering ex situ conservation. However, this by itself may not be easy to implement. Also it may not adequately represent the extant wild genetic variation of the species. Therefore, more emphasis should be given to the in situ conservation while increasing political commitment and conservation awareness among the local people.Present day forest patches of Guangua Wereda in NW Ethiopia are remnants after loss of habitat from a previously large and contiguous forest. Analysis of long-lived adult trees suggests the former forest probably exhibited strong spatial patterns of genetic structure (IBD). This means that protecting a range of patches including small and isolated ones is needed to conserve genetic diversity in this degraded landscape. This should, however, be supplemented with conserving trees growing in the surrounding farmland and pasture because matrix trees have high reproductive output and potential to act as links between populations . NeverthPrunus africana is an endangered evergreen Afromontane medicinal tree species endemic to sub-Saharan Africa and nearby islands [Prunus africana[P. africana is assisted by insects and fruits start to develop within 4-6 months after pollination . Fruit ripening usually occurs between September and November but in Kakamega forest of Kenya, Farwig et al. [P. africana seem to be recalcitrant. Fresh seeds can readily germinate under shady conditions [ islands . No reli africana but we g africana. The flog et al.  observedg et al. . Seeds onditions . The spenditions .Eragrostis tef), wheat, barley and rarely beans. The matrix also has some range lands or pastures. Both the matrix of the pasture and agricultural land contained a few remnant tree species including Prunus africana.The study sites were located in the fragmented landscapes of Awi Zone, Gojjam, NW Ethiopia, at about 10°45’-11°04’ N and 36°25’-36°48’ E. This is a floristically impoverished region where all but a few remnant forest patches have been destroyed, mostly by anthropogenic means. The major rainy season extends from June to October. Mean annual rainfall is about 1500 mm and mean annual temperature ranges between 19°C and 30°C. Elevation of remnant forest patches ranges between approximately 1750 and 2390 m. Matrix surrounding remnant patches is dominated by agricultural land cultivated in various shifts for millet, corn, tef (Maps of the forest patches were digitized from high resolution satellite images of the study region. The digitized images were processed in Arc-Map10 (ESRI Inc.) to compute patch area and distance to the nearest patch (a measure of patch isolation). Following this, four large 650 -1389 ha) and four small (8 - 62 ha) remnant study patches were selected  of each fruiting tree was measured using a measuring tape. Height was visually estimated. Where available, up to 6 seedlings (estimated height 5 cm to 30 cm) per fruiting tree were sampled near the canopy cover of selected fruiting individuals. Two hundred and thirty two seedlings were processed. Readings of elevation and geographical position were taken for each sampled fruiting tree and seedling. Leaf and bark samples were dried in locked plastic bags containing crystals of silica gel. Samples were stored in a -80°C freezer until required for DNA extraction.In each patch, About 0.25 g of dried leaf and bark samples were machine-ground using a TissueLyser II and 3 mm tungsten beads (QIAGEN Inc.). The TissueLyser grinding was operated at a frequency of 30/s in two minute interval up to 7 times or until samples were finely powdered. Genomic DNA was extracted following a modified Cetyltrimethylammonium bromide (CTAB) protocol . This prP. africana[2, 10 μM dNTP, 10 μM forward primer, 10 μM reverse primer, 10 mg/ml BSA, 10 μM fluorescent-labelled M13(-21) primers , 1 U/μl Red Taq DNA polymerase (PROMEGA) and about 10 ng of template DNA. The forward primer had the universal M13 tail where the labelled M13 primer can attach. Optimized PCR conditions consisted of a cycle of initial denaturing at 94°C for 5 minutes followed by 35 cycles of denaturing at 94°C for 30 s, annealing at 55°C for 45 s and extension at 72°C for 45 s. The final extension of amplicons was set at 72°C for 10 minutes. The PCR products were subjected to electrophoresis to isolate and size microsatellite fragments against an internal LIZ size standard (GeneScan500(-250)LIZ) on a 3130 Genetic Analayzer (APPLIED BIOSYSTEMS). Allele scoring was performed in GeneMapper 4.1 (APPLIED BIOSYSTEMS). Genotyped microsatellite data were tested (after 1000 randomizations) for occurrence of null alleles, stuttering or large allele dropout using MICRO-CHECKER version 2.2.3 [Eight nuclear microsatellite DNA markers  originally developed for other species of the same genus but later transferred to  africana were useon 2.2.3 . These ton 2.2.3 .All statistical analyses were performed separately for adults and seedlings as well as for the overall genotype data set consisting of both adults and seedlings. To assess deviation of genotypes from Hardy-Weinberg equilibrium, exact tests  were per10 transformed prior to analysis to approximate normality. To meet the basic assumptions for the linear regression analyses we always performed residual diagnostics for normality, homoscedasticity, linearity and independence problems. Fortunately, we did not encounter any problem regarding these assumptions. The level of variance in genetic diversity based on allelic richness among the two patch size  and patch isolation (less-isolated vs isolated) groups was also evaluated using the Student’s t-test in SAS 9.2 (SAS Institute Inc.). Patch size and isolation groupings were made using median values for size and isolation. The existence of inbreeding was evaluated by comparing both the inbreeding coefficient (FIS) and corrected genetic relatedness (Relc) among the two population groups as implemented in FSTAT. Mean allelic richness, gene diversity, number of alleles and inbreeding coefficient (FIS) were compared between cohorts using paired t-test in SAS 9.2 (SAS Institute Inc.).We determined unbiased genetic diversity parameters  for each population at 7 polymorphic microsatellite markers as implemented in FSTAT version 2.9.3.2 . AllelicFST) and testing for significance after 10,100 permutations as implemented in ARLEQUIN version 3.5.1.2 [FST values were compared between cohorts using paired t-test and between patch size and isolation groups using the Student’s t-test in SAS 9.2 for the overall data set. The mean population-specific FST values (see below) were also linearly regressed against patch size or isolation in SAS 9.2 based on the overall data set. The spatial pattern of genetic differentiation among remnant forest patches was assessed by running a Mantel test [FST) and geographic distances (point distance measured in Km between centres of patches) between pairs of populations. Partial Mantel tests were performed in ARLEQUIN applying 100,000 permutations to check the correlation of matrices of the genetic and two environmental variables  while keeping constant one of these environmental variables . Spatial genetic patterns were also assessed by applying a hierarchical Bayesian method to compute population-specific FST values and relate them to environmental variables through a generalized linear model in GESTE version 2.0 [Population differentiation and extent of genetic structuring was evaluated by computing pairwise population genetic distances ( 3.5.1.2 . Averagetel test  in ARLEQsion 2.0 . All parFST (F’ST) and Jost’s Dest[FST on the genetic diversity of cohorts. Computation of these estimates was necessary because uncorrected FST comparison of seedlings and adults could be compromised by differences in their genetic diversity. Tests for recent reduction in effective population size were carried out in Bottleneck version 1.2.02 [p-value of 0.01.Standardized st’s Dest,56 were st’s Dest,76 to min 1.2.02 . These an 1.2.02 . Becausen 1.2.02 , statistn 1.2.02  using thn 1.2.02 . Ten thon 1.2.02  were runafricana: Prunus africana; CTAB: Cetyltrimethylammonium bromide; PCR: Polymerase chain reaction; Km: Kilometer; Ne: Effective population size; TPM: Two-phase mutation model; IBC: Institute of biodiversity conservation; DAFF: Department of agriculture, fisheries and forestry; Hs: Gene diversity; FIS: Inbreeding coefficient; MNA: Mean number of alleles; CITES: Convention on international trade in endangered species of wild fauna and Flora; IUCN: International union for conservation of nature; ICRAF: World agroforestry centre.NW: North-western; IBD: Isolation by distance; P: Authors declare that they have no competing interests.HY conceptualized, designed and conducted the field and laboratory works, analyzed data, and drafted the manuscript. DS conceptualized this work, guided in data analyses and critically improved the draft manuscript for intellectual content. JH conceptualized and designed the study, guided and commented all data analyses and critically improved the draft manuscript for intellectual content. All authors read and approved the final manuscript.We show in this table environmental variables and genetic diversity estimates of Prunus africana in NW Ethiopia based on the overall data set.Click here for fileIn this file we illustrate linear regression of fragmentation variables against allelic richness and population-specific FST of Prunus africana in NW Ethiopia .Click here for fileHere the results of the Wilcoxon’s test for population bottlenecks under TPM and SMM are provided.Click here for fileThis table shows sample size and proportion of first generation migrants in the seedling and adult populations of P. africana in NW Ethiopia.Click here for fileThis photographic file demonstrates human impact and the occurrence of extensive logging and felling of age-old P. africana trees in the remnant forest patches of NW Ethiopia.Click here for file


This paper provides a brief overview of, and elaborates on, some of the presentations, discussions and conclusions from Day 4 of the ‘WHO EURO 2014 International Healthy Cities Conference: Health and the City - Urban Living in the 21st Century’, held in Athens, Greece on 25 October 2014. The Internet of Things (IoT) is made of sensors and other components that connect our version of the world made of atoms, i.e., humans/our bodies, our devices, vehicles, roads, buildings, plants, animals, etc., with a mirror digital version made of bits. This enables cities and regions to be self-aware and dynamically reconfigurable in real- or near-real-time, based on changes that are continuously monitored and captured by sensors, similar to the way the internal biological systems of a living being operate and respond to their environment (homeostasis). Data collected by various IoT sensors and processed via appropriate analytics can also help predict the immediate future with reasonable accuracy, which enables better planned responses and mitigation actions. Cities and regions can thus become more adaptable and resilient in face of adversity. Furthermore, IoT can link atoms (humans) to other atoms (humans) (again via bits), resulting in the formation of ‘smart(er) communities’ that are socially connected in new ways and potentially happier. Cities, but also less urbanised regions and the countryside, could all benefit from, and harness the power of, IoT to improve the health, well-being and overall quality of life of the local populations, actively engage citizens in a smarter governance of their region, empower them to better care for one another, promote stronger social inclusion, and ensure a greener, sustainable and more enjoyable environment for all. Technology can also help reverse the ‘brain drain’ from the countryside and smaller towns to larger metropolises by making the former more attractive and connected, with better services akin to those found in larger cities. The article also discusses some ways of measuring and benchmarking the performance of smart cities and their impact on well-being. However, it should be emphasised that technology is not a panacea and that other factors are equally important in creating happier and healthier cities and regions.The online version of this article (doi:10.1186/1476-072X-14-3) contains supplementary material, which is available to authorized users. The The last day of the conference, Day 4 (25 October 2014), focused on the theme of ‘smart and innovative cities ’, discussing among other topics: ‘how can we create and measure happy and resilient cities’ and the roles and challenges posed by Internet of Things (IoT) technologies in the realisation of happier cities [‘Creating self-aware and smart healthy cities’ was the title of the plenary keynote by Maged N. Kamel Boulos, Professor and Chair of Digital Health, Alexander Graham Bell Centre for Digital Health, University of the Highlands and Islands, Scotland, UK . Christia self-configuring and adaptive system consisting of networks of sensors and smart objects whose purpose is to interconnect” all “things, including every day and industrial objects, in such a way as to make them intelligent, programmable and more capable of interacting with humans” [A major theme of Day 4 was the Internet of Things (IoT) and its role in creating smart healthy cities. The IEEE IoT Technical Community defines IoT as “ humans” . IoT is  humans” . This en humans” , based o humans” , 13.In his plenary keynote on Day 4   and where it happened (or is happening). Anticipatory or predictive analytics focus on what could happen next based on past and present data, so that cities and regions can be best prepared to take appropriate action, while prescriptive analytics deal with the latter, i.e., with identifying the best course of action to take in response to an anticipated issue from among one or more available alternative options and in light of any constraints, resource availability and other factors or requirements that should be considered , 19. HowIoT relies on a growing number of sub-technologies and subsystems that need to be seamlessly interconnected and interfaced with one another in real time. This can only be achieved through the adoption of adequate standards and protocols for measurement, communication, integration, interoperability and control , 13. LogIoT data and device privacy and security are among the most pressing challenges facing IoT-driven smart cities . Users oBesides linking a physical world of atoms to a mirror world of bits, IoT can link atoms (people) to other atoms (people) via bits, resulting in the formation of ‘smart(er) communities’ that are socially connected in new ways and potentially happier. Cities, but also less urbanised regions and the countryside, could all benefit from, and harness the power of, IoT to actively engage citizens in a smarter participatory governance of their region , empowerraison d’être and its real ‘head’. Perhaps a better term here would be the ‘Internet of Things and People’. IoT-driven smart cities are most successful and smartest when their focus is on people, and when they actively involve and engage their citizens in co-creating , co-running and co-monitoring sensing’ , 30.. e-labour exchange on Elance [Digital inclusion (e-inclusion) programmes such as ‘training the trainers/digital community ambassadors’ and Internet training programmes targeting older and disadvantaged people  thus becn Elance , doing sn Elance  would fuHelping and supporting others can make people happier. Lee describes how IoT can help users form a variety of communities of practice (smart communities) for enhancing the quality of life and curating available expertise in the community, giving the example of a smart home community-based service to allow collective baby and child care among neighbours . At the While acknowledging that there is ‘no-one-size-fits-all’ IoT-driven smart city solution for cities of different sizes, needs and settings, the ‘economies of scale’, determined by the size of the population to be served by a given IoT service, usually play an important and decisive role when deploying sustainable smart services that are cost-efficient and cost-effective, and thus good value for taxpayers’ money. Smaller towns and villages tend to be left at a disadvantage because of their much smaller population sizes and modest local capacities and resources, all of which make the deployment of some IoT-driven smart services prohibitively expensive and non-viable at such limited scales. However, there is no reason why the populations of smaller and rural settlements should be left lagging behind, especially when one considers the negative consequences in terms of the difficulty these regions have in retaining their best local skills and stopping the eventual ‘brain drain’ of young professionals to the surrounding larger and more attractive cities.. a metropolis and its suburbs consisting of multiple smaller cities and towns).It has been said there is ‘power in unity’, and this cannot be truer than in the case of small neighbouring towns and villages  uniting together to reach the required economies of scale when their local populations and resources are pooled together under some suitable single administrative umbrella to form a larger ‘distributed city’. But for this pooling and the subsequent deployment of smart services to occur and succeed, the smaller settlements within a distributed city will need to be well connected with each other and the rest of the country by a good network of physical roads and/or highways and most importantly by reliable, high-speed cable and mobile Internet  that is still preferred by many.Mapping Smart Cities in the EU’ [vis-à-vis Europe 2020 [In January 2014, Antoni Vives, Barcelona’s Deputy Mayor for Urban Habitat, reported that his city was able to make big savings of more than €75 million in areas such as smart water, lighting and parking management, besides creating 47,000 new jobs related to smart city developments . A Januaope 2020  targets,Fachbereich Stadt- und Regionalforschung - SRF) at Vienna University of Technology (Technische Universität Wien) within PLEEC (PLanning for Energy Efficient Cities), an EU-funded Framework Programme 7 (FP7) project [The European Smart City Model is a performance ranking and benchmarking tool for medium-sized cities with an urban population between 100,000 and 500,000. Version 3.0 2014) of the tool covers 77 European medium-sized cities; larger cities and metropolises, such as the Spanish city of Barcelona, are excluded from the model by design. The model considers a city to be smart if it is performing well in six areas, namely Smart Economy, Smart Mobility, Smart Environment, Smart Governance, Smart Living and Smart People. Cities can be ranked and compared according to how well each city is performing in these areas  [The ultimate outcomes of happiness and well-being can be more tricky to measure, but the OECD experience in this respect is quite notable, with their Better Life Index that goes beyond the cold numbers of GDP (Gross Domestic Product) and conventional economic statistics, covering 11 areas of material living conditions and quality of life, namely Housing, Income, Jobs, Community, Education, Environment, Civic Engagement, Health, Life Satisfaction, Safety and Work-Life Balance. Each of these topics affects people’s well-being and happiness in varying ways, and is measured by one to four indicators. Some, but not all, of these topic indicators are based on self-reported and other subjective data, e.g., from surveys that measure life satisfaction and happiness. The indicators attempt to capture those aspects of a topic that are most relevant to producing well-being under that topic category , 40. Forucation) . One of ucation) , 40. Theucation) , 43.FiguThe digital revolution can be harnessed as a great tool for tackling various inequalities, but this is not yet the case in many countries. The World Wide Web Foundation’s Web Index 2014–15 Report  evaluateINKA - INnovatiiviset KAupungit, 2014–2020) run by Tekes, the Finnish Funding Agency for Innovation, aims at creating competitive, high-tech companies and top-notch talent to promote the emergence of innovation clusters in Finland capable of developing brand-new products and services for an international market. Five themes were selected for the programme: Smart City and Renewable Industry, Bioeconomy, Sustainable Energy Solutions, Future Health, and Cybersecurity. An urban region was appointed to take charge of each theme , with seven other urban regions selected to partner in the five themes [The Global Liveability Ranking and Report August 2014) by the Economist Intelligence Unit ranked Helsinki eighth in the Liveability Ranking  . Helsink by the Ee themes , 47, 48.e themes .An emerging field combining Finland’s long history of digitised healthcare information and the rapidly growing mainstream video gaming industry is ‘Games for Health’ –52. ThesIoT represents the fourth industrial revolution, opening up opportunities for a more enjoyable life and new and better ways of doing business. While IoT will create entirely new business models, innovations will have to come more quickly for companies to stay relevant. To do this, managers need to envision the valuable new opportunities that become possible when the physical world is merged with the virtual world, and where potentially every physical object can be both intelligent and networked. Starting now, they must create the organisations and IoT-based business models that can turn these ideas into reality” [Innovation is key to avoiding collapse and promoting sustainability of cities/regions and their infrastructures . IoT forreality” . Cities reality” . But ‘peThe 2015 Annual Healthy Cities Business and Technical Conference will take place in Kuopio, Finland, from 24–26 June 2015 , and it Slides: Creating self-aware and smart healthy cities – by MN Kamel Boulos.(PDF 4 MB)Additional file 1:


Sander vitreus, an ecologically and economically valuable North American temperate fish with large Laurentian Great Lakes' fisheries. This study compares genetic diversity and divergence patterns across its widespread native range using mitochondrial (mt) DNA control region sequences and nine nuclear DNA microsatellite (μsat) loci, examining historic and contemporary influences. We analyze the genetic and morphological characters of a putative endemic variant– “blue pike” S. v. “glaucus” –described from Lakes Erie and Ontario, which became extinct. Walleye with turquoise-colored mucus also are evaluated, since some have questioned whether these are related to the “blue pike”.Conserving genetic diversity and local adaptations are management priorities for wild populations of exploited species, which increasingly are subject to climate change, habitat loss, and pollution. These constitute growing concerns for the walleye FST mtDNA = 0.32 ± 0.01, μsat = 0.13 ± 0.00) and substantial diversity across their range . Southern populations markedly differ, possessing unique haplotypes and alleles, especially the Ohio/New River population that houses the oldest haplotype and has the most pronounced divergence. Northern formerly glaciated populations have greatest diversity in Lake Erie . Genetic diversity was much less in the historic Lake Erie samples from 1923–1949  than today. The historic “blue pike” had no unique haplotypes/alleles and there is no evidence that it comprised a separate taxon from walleye. Turquoise mucus walleye also show no genetic differentiation from other sympatric walleye and no correspondence to the “blue pike”.Walleye populations are distinguished by considerable genetic divergence (mean Contemporary walleye populations possess high levels of genetic diversity and divergence, despite habitat degradation and exploitation. Genetic and previously published tagging data indicate that natal homing and spawning site philopatry led to population structure. Population patterns were shaped by climate change and drainage connections, with northern ones tracing to post-glacial recolonization. Southerly populations possess unique alleles and may provide an important genetic reservoir. Allelic frequencies of Lake Erie walleye from ~70–90 years ago significantly differed from those today, suggesting population recovery after extensive habitat loss, pollution, and exploitation. The historic “blue pike” is indistinguishable from walleye, indicating that taxonomic designation is not warranted. Species today face many challenges that influence their genetic, phenotypic, and ecological diversity and divergence patterns across space and time. The genetic variation of their component populations comprises the raw material underlying overall and local adaptedness, providing resilience to anthropogenic stressors –such as climate change, habitat alteration and loss, invasive species, and exploitation ,2. NotabPopulation genetic patterns of today’s temperate taxa resulted from historic and contemporary processes ,8, regulDuring the Pleistocene Epoch, ~2.6–0.01 million years ago, the North American Laurentide Ice Sheet advanced south to the Ohio River system  and cutthroat trout Oncorhynchus clarkii (Richardson 1836) .Both “blue pike” and walleye shared a popular commercial fishery with the former collapsing in 1959 attributed to exploitation, pollution, and/or habitat alteration and the latter concurrently declining . The las. Specific questions are: (1) What is the genetic structure of walleye across its native distribution?, (2) How does genetic diversity vary across the range?, (3) What is the relationship of contemporary samples to historic Lake Erie (1923–1949) patterns?, and (4) Did the extinct “blue pike” significantly differ from walleye?This study aims to resolve population genetic patterns of walleye across its native range, providing a baseline for evaluating future anthropogenic pressures. We analyze contemporary walleye spawning groups and compare Lake Erie populations to historic samples, including the putative “blue pike” variant. Morphological characters also are analyzed for the historic samples. We expand the previous sampling coverage to 1181 walleye and combine information from mtDNA control region sequences and nine nuclear DNA μsat loci. Patterns are evaluated at multiple evolutionary and temporal scales, with mtDNA sequences revealing historical context  and μsat loci addressing contemporary microevolutionary processes  ,51. BiogContemporary walleye populations contain 27 control region haplotypes Figure . The remFive abundant haplotypes  comprise 90% of walleye individuals, with haplotype 1 being the most frequent and widely distributed Figure . HaplotyEach spawning group contains multiple haplotypes Figure , Table 1HD) varies greatly among populations .Haplotype diversity . Locus Svi18 has the fewest alleles and Svi6 the most. Seven loci are included here for historic walleye and “blue pike”, since Svi17 and L7 failed to amplify. Results from the seven versus the nine loci are identical among the 23 contemporary spawning groups, therefore, the former are presented here; the dataset based on nine loci was detailed by Stepien et al.  [The data sets supporting the results of this article are available in the Dryad digital repository [d.nk470]  and GenBThe authors declare that they have no competing interests.CAS conceived the project. AEH collected, analyzed, and interpreted the molecular and morphological data, drafted, and revised the paper. CAS oversaw the project design, assisted in data interpretation, added major parts and revisions to the paper, and extensively edited it. Both authors have read and approved the final manuscript.This work formed part of the Ph.D. dissertation work of AEH in the laboratory of CAS, who now is a postdoctoral associate in the laboratory. CAS has worked on walleye genetic patterns for over two decades, and her Great Lakes Genetics/Genomics laboratory focuses on invasive species, as well as native fishes.Phylogenetic tree of contemporary and historic walleye mtDNA control region haplotypes. Above branches = Bayesian posterior probabilities (pp) and Maximum likelihood bootstrap pseudoreplications. Nodes with ≥0.50 pp and ≥50% bootstrap support are reported. * = haplotypes found in historic walleye and “blue pike” samples; the latter had no unique haplotypes. All “blue pike” individuals had haplotype 3, which is one of the most common walleye haplotypes.Click here for fileMtDNA control region haplotype frequencies for population samples, including 23 contemporary walleye spawning groups, historic Lake Erie walleye, and “blue pike”.Click here for fileSummarized genetic variation per microsatellite locus for population samples, including 23 contemporary walleye spawning groups, historic walleye, and “blue pike” samples, totaling 1181 individuals. Table shows PCR annealing temperature (TA), number of alleles (NA), allelic size range , genetic deviation across all combined samples (FIT), mean genetic divergence among loci (FST), inbreeding coefficient , and neutrality test from the program Lositan [ Lositan .Click here for fileAllelic sizes and distribution for seven nuclear μsat loci among samples, including: contemporary walleye from Lakes Erie (the western and eastern basins) and Ontario, historic Lake Erie walleye, and the “blue pike”. Site labels (letters) match those in Table Click here for fileGenetic isolation by geographic distance comparison among 23 contemporary spawning groups of walleye. A mtDNA control region  and B seven nuclear μsat loci . Results from the seven loci are identical to those for nine loci . LetterClick here for filePairwise exact tests of genetic differentiation among population samples (lettered) for control region sequence data  and seven nuclear μsat loci . Results are congruent to FST comparisons and those from the seven nuclear μsat loci data are identical to values calculated based on nine loci , with dn et al. , bold = Click here for fileGeneclass2 [ assignments among contemporary walleye spawning groups. Values = percentage assignment, parentheses = number of individuals assigning to that group, bold = self-assignment, and italics = greatest assignment. Numbers in parentheses next to spawning group denote sample size.eclass2  assignmClick here for fileGraph of K vs. ΔK based on Evanno et al. [, showing the optimal K A contemporary walleye spawning groups and B contemporary Lakes Erie and Ontario and historic Lake Erie walleye (with “blue pike”). et al. , showinClick here for filePopulation genetic distance neighbor joining tree for contemporary walleye Sander vitreus vitreus spawning groups, historic walleye, and “blue pike” S. v. “glaucus” in relation to sauger S. canadensis. Tree is based on combined frequencies of mtDNA control region haplotypes and alleles from the seven nuclear μsat loci.Click here for fileLatitude (Lat.) and longitude (Long.) for each contemporary walleye spawning group used in analyses. Letters correspond to locations from Figure Click here for fileMaterials examined, including A museum specimens and B additional contemporary specimens that augmented prior work reported by Stepien et al. . Institutional abbreviations follow Leviton et al. [n et al. . Sample Click here for file


Populations often contain discrete classes or morphs  characterized by distinct patterns of trait expression. In quantitative genetic analyses, the different morphs can be considered as different environments within which traits are expressed. Genetic variances and covariances can then be estimated independently for each morph or in a combined analysis. In the latter case, morphs can be considered as separate environments in a bivariate analysis or entered as fixed effects in a univariate analysis. Although a common approach, we demonstrate that the latter produces downwardly biased estimates of additive genetic variance and heritability unless the quantitative genetic architecture of the traits concerned is perfectly correlated between the morphs. This result is derived for four widely used quantitative genetic variance partitioning methods. Given that theory predicts the evolution of genotype-by-environment (morph) interactions as a consequence of selection favoring different trait combinations in each morph, we argue that perfect correlations between the genetic architectures of the different morphs are unlikely. A sampling of the recent literature indicates that the majority of researchers studying traits expressed in different morphs recognize this and do estimate morph-specific quantitative genetic architecture. However, ca. 16% of the studies in our sample utilized only univariate, fixed-effects models. We caution against this approach and recommend that it be used only if supported by evidence that the genetic architectures of the different morphs do not differ. The fundamentals of quantitative genetics Fisher  provide Additive genetic variances are defined within the context of a specific population and environment. However, traits are often expressed in two different environments or phenotypic classes within a single population. This is true of dimorphic traits such as disease incidence, wing dimorphisms, protective dimorphisms, trophic dimorphisms, mating dimorphisms, and life cycle dimorphisms . Although this is necessary to control for fixed differences in phenotypic means between the morphs, it does not affect the correlation between morphs in breeding values . By itself, using a fixed effect of morph invokes the biological assumption of a perfect additive genetic correlation between the two morphs. Although the statistical implications of this assumption are generally understood, neither in the quantitative genetic literature nor the evolutionary ecology literature have the effects of this assumption on quantitative genetic parameter estimates been formally quantified and thoroughly explained. Consequently, key quantitative genetic parameters in evolutionary ecology are not being estimated  or are potentially being miscalculated . In this study, we describe the bias in estimates of additive genetic variance that arises when the additive genetic effects of a trait are assumed to be perfectly correlated between two morphs. As expected  for the trait in the two morphs of a population can be described by a bivariate normal distribution, where each has a mean of zero, a variance according to the morph-specific variance in trait breeding values [Var(a1) and Var(a2)], and some correlation between breeding values in the two morphs  can be described by a univariate normal distribution with a mean of zero and variance equal to the variance in average breeding values, Var(au) =Var(a2)], an algebraic rearrangement of equation Var(au), will be less than both Var(a1) and Var(a2) whenever the between-morph additive genetic correlation is less than unity. This is seen in FigureIllustrations of the morph M1, morph M2, and average breeding value distributions, using one set of random draws from each respective distribution, are shown in FigureVar(a1)≥Var(a2), a rearrangement of the right hand side of equation Var(au), will be less than either of the two morphs’ additive variances wheneverAssuming that Vara≥Var(a2),Var(a2) is 10% less than Var(a1). See Appendix  1 for the analogous equation predicting the between-morph genetic correlation at which the heritability of the morphs combined will be less than either of the morph-specific heritabilities.This downward bias is illustrated in FigureIn practice, true breeding values are unknown, and thus, the additive genetic variance must be estimated using known contributions of additive variance to the phenotypic resemblance between relatives. For many breeding designs, the additive genetic variance is estimated as a fraction of the covariance between offspring and parent phenotypes or as a fraction of the sire or dam variance components Falconer . AlternaMethods of estimating the additive genetic variance  from the covariance between offspring and parents, or the variance among half-sib families also depend on there being no genotype-by-morph interaction for breeding values expressed in two different morphs variance components. The additive genetic (co)variances can then be estimated from sire, dam, and within-family (co)variances . The breeding values in a are assumed normally distributed with mean of zero and variance of Var(a) = Ga⊗A, where A is the additive genetic relationship matrix (⊗ symbolizes the direct product between two matrices). In this model, Ga = σ2a where σ2a is the additive genetic variance in the base population. Thus, the assumption regarding the relationship between morph M1 and M2 breeding values for the univariate model . In such a model, only morph M1 traits are expressed in morph M1 and only morph M2 traits are expressed in morph M2. Accordingly, all morph M1 individuals will have missing phenotypes for the morph M2 trait and vice versa for morph M2 individuals. The intercept, or overall mean, of the model accounts for a difference in the means of unstandardized phenotypes for morphs M1 and M2. This approach makes a residual covariance between the two morphs impossible to define as no individual can express the trait in both morphs  are multivariate normally distributed. Consequently, Var(a) = Ga⊗A, but here Ga is the 2 × 2 matrix: In the bivariate model . Thus, Ga in equation σ2a-1=σ2a-2=σa-1,2 , and the bivariate model (equation S1.2) is equivalent to the univariate model (equation S1.1). When these assumptions are valid , mixed effect models treating any differences between the morphs as a fixed difference  will produce unbiased estimates of the additive genetic variance in the population. However, if the between-morph additive genetic correlation is less than unity  as illustrated in FigureWhen a univariate animal model only includes morph as a fixed effect, the separate distributions of breeding values for the two morphs are assumed perfectly correlated i.e., ra-,2 = 1. Tequation .2 is equGryllus firmus. They initially estimated heritability when the sexes or wing morphs were combined followed by analyses where sex and wing-morph-specific heritabilities were estimated. For a number of traits, the combined estimates of heritability were lower than the sex-specific or wing-morph-specific estimates. They postulated that these traits had between-sex or between-morph genetic correlations less than one. In agreement with their predictions, and our predictive equations, Roff and Fairbairn , birds (21 papers), and plants (11 papers). We found 79 papers estimating quantitative genetic parameters, 63 of which studied traits expressed by discrete morphs or classes within a population or breeding design. Sex was the most common dimorphic trait (35 of 63), but discrete environments, resource availabilities , ages, or populations/lines were also common. Note, however, that morph classifications are not mutually exclusive. For example, Ingleby et al.  studied Only one of the 63 studies ignored the effects of the discrete morphs in their data. Of the studies that explicitly considered dimorphism (or polymorphism) in their analyses, 53 included a fixed effect (see Appendix Anser anser) and found that the sex-specific heritability estimates  gonadal size across months given the dataset available. Instead, they allowed for month-specific additive genetic variances by fitting separate models for each month. These studies illustrate the recommended practice of including dimorphic (or polymorphic) trait variation in analyses and only simplifying the models after confirming similar morph-specific additive genetic variances and between-morph additive genetic correlations of approximately one.In the absence of supplemental models or previous estimates from the study populations, it is not possible to determine whether the additive genetic variance estimates are biased downward in the 10 studies that relied only on univariate models .Estimates of additive genetic variance are at the heart of many studies in evolutionary ecology that are conducted to answer general questions regarding (1) the evolutionary forces that shape additive variance; and (2) population responses to selection. Discrete morphs or phenotypic classes occur in some species by virtue of different patterns of gene expression. There is no a priori reason to assume that the patterns of variances within and covariances among traits should be the same for traits expressed in these two genetic environments. Therefore, initial estimates of variances and covariances should consider the separate morphs or classes as distinct with the potential for genotype-by-morph interactions between them. As demonstrated above, when such genotype-by-morph interactions are not explicitly considered the resulting variance in the joint distribution of breeding values will be less than the variance in breeding values for either class. Thus, the estimated effect of evolutionary forces on additive genetic variance and predictions for evolutionary responses in mean phenotype may differ substantially based on the way phenotypes are modeled in a quantitative genetic analysis . The common practice from papers in our literature sample (ca. 84%) and the approach we argue for here is to estimate morph-specific additive genetic variances and only combine traits across morphs when there is no evidence for morph-specific genetic architecture variances are Downward biases in estimates of additive genetic variance exacerbate problems with quantitative genetic inference in limited datasets . Such datasets often do not have the sample size or informative relationships necessary to disentangle additive variance from other sources of phenotypic resemblance among relatives (e.g., see discussion above of Schaper et al. Although the arguments made above have been framed within a single trait context for simplicity and ease of interpretation, the results extend to multivariate trait relationships where the pattern of covariances among traits will often differ between classes as well. This point is particularly salient for predicting evolutionary change using the statistical relationship between breeding values of a trait and of relative fitness (Secondary Theorem of Natural Selection; Robertson


In recent years, small-area-based ecological regression analyses have been published that study the association between a health outcome and a covariate in several cities. These analyses have usually been performed independently for each city and have therefore yielded unrelated estimates for the cities considered, even though the same process has been studied in all of them. In this study, we propose a joint ecological regression model for multiple cities that accounts for spatial structure both within and between cities and explore the advantages of this model. The proposed model merges both disease mapping and geostatistical ideas. Our proposal is compared with two alternatives, one that models the association for each city as fixed effects and another that treats them as independent and identically distributed random effects. The proposed model allows us to estimate the association (and assess its significance) at locations with no available data. Our proposal is illustrated by an example of the association between unemployment (as a deprivation surrogate) and lung cancer mortality among men in 31 Spanish cities. In this example, the associations found were far more accurate for the proposed model than those from the fixed effects model. Our main conclusion is that ecological regression analyses can be markedly improved by performing joint analyses at several locations that share information among them. This finding should be taken into consideration in the design of future epidemiological studies. In the last decade, the use of ecological regression studies taking small areas as units of study has become very popular. The objective of this type of study is to examine, from a geographical point of view, the relationship between a health-related outcome and exposure factors . When smRecently, a growing number of studies have been published on socioeconomic inequalities in mortality at the small-area level in non-adjacent cities (or metropolitan areas) . BrieTo our knowledge, only one study, by Martinez-Beneito et al., has conducted a joint analysis of all cities considered . That meSome of the previously-mentioned studies found a spatial pattern, at the inter-city level, of socioeconomic inequalities in mortality, suggesting spatial dependence between cities ,6,8. ThiIn this study, we propose a multilevel ecological regression model in which the first level of the analysis consists of small areas nested within cities and the second level corresponds to the cities themselves. This analysis allows the identification of possible spatial structures at both of the levels considered. This proposal is illustrated by an example from real data exploring the association between unemployment and lung cancer mortality among men in 31 Spanish cities from 2002 to 2007.Oij denotes the number of observed cases in census tract i  of city j, the administrative divisions used from now on to introduce the models below. Similarly, Eij denotes the expected number of cases, θij the relative risk, and Xij the unemployment rate of the corresponding census tract.We propose three different Bayesian hierarchical models for performing multilevel ecological regression, all three corresponding to different assumptions. For all proposals, θij within each city. To do this, we consider two random effects, as proposed by Besag, York and Mollié (BYM). The spatial random effects for each city j (Sij) are assumed to follow an intrinsic conditional auto-regressive distribution (ICAR) [Hij) are assumed for each city to follow independent normal distributions with mean zero and variance, σS j and σH j) of the random effects [As usual in disease mapping studies, we account for the spatial structure of the relative risks n (ICAR)  with dif effects . Note thIn addition to these census-tract-level random effects, each city also has a linear component that depends on the covariate of interest, so that the relationship between this factor and the risks can be explored. We assumed the effect of both the intercept and the covariate to be completely different and independent for each city. Thus, M1 can be formulated as follows:l being a suitable upper limit, making the corresponding uniform distributions vague. This model is equivalent to performing separate ecological regressions (with BYM random effects) for each city which, as mentioned in the Introduction, is the most common procedure when analysing multiple cities in a single study. This model and those that follow will return the RR per 1% increase in the unemployment rate for each city j (exp(bj2)) as the main outcome that summarises the relationship between the covariate and the observed counts under study.For bj1 and the coefficients associated with the covariate bj2 are considered to be random effects rather than fixed effects for each city (Model 1). Specifically, both bj1 and bj2 are assumed to follow normal distributions with means and variances that are unknown but common to all the cities. In contrast to M1, information on the values of bj1 and bj2 for j = 1,…,31 will now be shared between cities through their standard deviations. The elements of these two vectors are not independent, as in M1, but are conditionally independent, given This model is based on M1, but now the intercepts bj2)), this model estimates an overall pooled RR for all cities (exp(β)), or, more precisely, for the set of cities from which the available cities are drawn. This common RR cannot be estimated in M1 because the RR in that model are considered as fully independent between cities and therefore it would make no sense to derive any joint estimate in M1.In addition to the RR of death for each city , now M3 also accounts for spatial dependence between cities using a geostatistical approach . Specifig et al. .bj2 depends on σ2, the standard deviation controlling the magnitude of these values and a correlation matrix (similarly for bj1). This matrix depends on D, the matrix of distances between cities and ϕ2, which controls the decay of spatial dependence as a function of the distance between cities. Within this model, we can also calculate R = −ln(0.05)/ϕ2, usually known in geostatistics as the effective range [β) can be interpreted as an overall pooled RR for the whole study, as in M2.The covariance matrix of ve range , which iAll models were fitted using a fully Bayesian approach. Posterior distributions were obtained by means of Monte Carlo methods based on Markov chains using the WinBUGS program (version 1.4.3)  executedOne of the main advantages of M3 is that, by integrating a geostatistical approach, it allows us to predict the RRs in locations where there are no available city data. Thus, the RR can be extrapolated to places where they cannot be directly observed, simply by the proximity of each of these points to the cities for which information is available. Specifically, the RR can be estimated for any location in the region of the study if there is a city of the same kind as those considered in the analysis. That is, if the available cities were, for example, a sample of the large cities in a country, the predictions made would correspond to the RRs that could be expected at any geographical location if a large city were located there. This will make these spatial predictions comparable since they correspond to similar units instead of reproducing the demographic pattern of the region of study. To this end, we used Kriging Bayesian spatial prediction as specified in part 4 of Diggle et al. . FurtherWe used the deviance information criterion (DIC)  as a modhttp://www.proyectomedea.org/). The units of analysis were the census tracts of 31 Spanish cities . Mortality data were obtained from the mortality registers of the corresponding autonomous communities. Population data were obtained either from the register of inhabitants for each city or from the National Institute of Statistics. Finally, as a covariate, we included the 2001 unemployment rate, defined as the percentage of unemployed workers in the total labour force. This socioeconomic indicator was obtained from the 2001 Spanish Population and Housing Census. Unemployment was used as a surrogate of material deprivation, which has been previously claimed to be associated with lung cancer mortality [In this study, we analysed male deaths due to malignant neoplasm of the trachea, bronchus or lung (henceforth ‘lung cancer’) during the period 2002–2007. Specifically, we selected deaths classified in codes C33 and C34 of the International Classification of Diseases, 10The expected numbers of cases per census tract to be used in the above-mentioned models were calculated by indirect standardization, taking the age-specific  male lung cancer mortality rates for the full set of cities as the reference.For M3, we estimated the posterior mean of the RR, the posterior standard deviations of the RR and the probability of the RRs being greater than 1 in each of the 5,221 locations on a grid of points that we considered throughout mainland Spain. These estimates were calculated following the geostatistical approach mentioned above.M1 = 36408.6, DICM2 = 36392.1, DICM3 = 36389.0). According to Spiegelhalter et al. [First, after running all three models for the lung cancer dataset, model selection was performed using the DIC statistic, which indicated M3 as the model showing the best fit ; RRM2, 1.024 (1.014–1.034); RRM3, 1.025 (1.016–1.035). The widths of the CIs for M2 and M3 are reduced by ≥75% with respect to those of M1. Consequently, the use of one model or another can lead to very different conclusions.Comparison of the RRs for all cities  revealedAs previously mentioned, it is common to analyse each city separately and thus to report the results of M1, which show more variability. In our opinion, the joint analysis of all cities using M2 and M3 is more appropriate, since the use of random effects allows information to be shared between cities, taking into account that they are not isolated entities. Although M2 and M3 estimated similar RRs in our particular study, M3 may sometimes have particular advantages. For example, for cities with few small areas, the uncertainty of the posterior distribution of the RR will be very high (resulting in a wide 95% CI). In this case, both M2 and M3 will markedly improve the RR estimate, although, if there is a spatial pattern in the RRs, M3 will take advantage of it, returning more accurate estimates. Moreover, if the data show weak spatial dependence, M3 will be able to reproduce that kind of pattern by yielding a short effective range and therefore the use of a spatial model in that case should not be a problem.σ2, which quantifies the variability of RR between cities. Therefore, M3 provides two parameters , allowing objective comparison of any two geographical patterns.As described in the previous section, M3 may use (Bayesian) Kriging to predict the RR at locations where there is no available information. Estimating the full posterior distributions of the predicted RRs allows visualization of other estimates beyond their posterior means, as in M2 = 1.023 (95%CI = 1.019–1.028) and oRRM3 = 1.025 (95%CI = 1.019–1.031). Both models provided very similar posterior means for oRR, although the posterior variability of M3 was greater, a result of considering correlation in the data (dependence makes estimates less accurate). Therefore, considering the data as spatially independent, when they are not, would lead to erroneously optimistic estimates (in terms of their variability). Moreover, if M3 estimated very little variability between cities (σ2 ≈ 0), it would make particular sense to estimate an overall RR for the cities as a whole, instead of independent (and noisy) estimates of this quantity for each city.Finally, M2 and M3 also yield overall RRs (oRR) for the cities as a whole. The posterior means of the oRR for these two models are oRRIn this study we propose a new ecological regression model for studies in small areas of non-adjacent cities. This model merges a frequently used proposal in disease mapping, the BYM model, with Bayesian geostatistical models , both ofFor this kind of multicentre study, it is common to perform independent analyses for each node, in our case cities, and to subsequently merge their results. In this paper, we show that this procedure overestimates the uncertainty of RRs in comparison to models that jointly analyse all cities, sharing their information by means of random effects (M2 and M3). Furthermore, the fixed effects modelling of M1 makes their estimates only applicable to the set of cities considered in the study, that is, their results cannot be extrapolated to any other city, which limits the interest of the results. In this regard, the results from M2 and M3 are much more useful and general.In our illustration, although the results for M2 and M3 were similar in terms of the estimated RR for each city, in our opinion, M3 shows at least three clear advantages over M2. First, it allows us to account for the spatial dependence both within and between cities. Second, it allows us to objectively detect and quantify (using the effective range) the spatial structure in the data. Finally, RRs (and even their significance) can also be predicted at locations with no available data. In our opinion, because of these advantages and the ability of M3 to adapt to patterns showing different degrees of spatiality , it is the most appropriate model for this kind of study.Finally, when studying the association between a health outcome and an exposure factor, it may be useful both to "map" at locations where information is available and to visually assess the similarity of their spatial patterns. The proposed model provides a different approach, allowing this association to be directly plotted in a continuous map. In our opinion, this improves understanding of this association. For example, as an end result, this article describes the spatial distribution of socioeconomic inequalities in mortality, allowing identification of the areas with the greatest inequalities, which are therefore the most amenable to intervention. Thus, the proposed models may be a valuable tool for policy makers to make a more appropriate investment of resources.S1 Appendix(DOC)Click here for additional data file.