Multiple genomic solutions for local adaptation in two closely related species (sheep and goats) facing the same climatic constraints.

The question of how local adaptation takes place remains a fundamental question in evolutionary biology. The variation of allele frequencies in genes under selection over environmental gradients remains mainly theoretical and its empirical assessment would help understanding how adaptation happens over environmental clines. To bring new insights to this issue we set up a broad framework which aimed to compare the adaptive trajectories over environmental clines in two domesticated mammal species co-distributed in diversified landscapes. We sequenced the genomes of 160 sheep and 161 goats extensively managed along environmental gradients, including temperature, rainfall, seasonality and altitude, to identify genes and biological processes shaping local adaptation. Allele frequencies at putatively adaptive loci were rarely found to vary gradually along environmental gradients, but rather displayed a discontinuous shift at the extremities of environmental clines. Of the 430 candidate adaptive genes identified, only 6 were orthologous between sheep and goats and those responded differently to environmental pressures, suggesting different putative mechanisms involved in local adaptation in these two closely related species. Interestingly, the genomes of the 2 species were impacted differently by the environment, genes related to signatures of selection were most related to altitude, slope and rainfall seasonality for sheep, and summer temperature and spring rainfall for goats. The diversity of candidate adaptive pathways may result from a high number of biological functions involved in the adaptations to multiple eco-climatic gradients, and a differential role of climatic drivers on the two species, despite their co-distribution along the same environmental gradients. This study describes empirical examples of clinal variation in putatively adaptive alleles with different patterns in allele frequency distributions over continuous environmental gradients, thus showing the diversity of genetic responses in adaptive landscapes and opening new horizons for understanding genomics of adaptation in mammalian species and beyond.

Seascape genomics reveals candidate molecular targets of heat stress adaptation in three coral species.

Anomalous heat waves are causing a major decline of hard corals around the world and threatening the persistence of coral reefs. There are, however, reefs that have been exposed to recurrent thermal stress over the years and whose corals appear to have been tolerant against heat. One of the mechanisms that could explain this phenomenon is local adaptation, but the underlying molecular mechanisms are poorly known. In this work, we applied a seascape genomics approach to study heat stress adaptation in three coral species of New Caledonia (southwestern Pacific) and to uncover the molecular actors potentially involved. We used remote sensing data to characterize the environmental trends across the reef system, and sampled corals living at the most contrasted sites. These samples underwent next generation sequencing to reveal single nucleotide polymorphisms (SNPs), frequencies of which were associated with heat stress gradients. As these SNPs might underpin an adaptive role, we characterized the functional roles of the genes located in their genomic region. In each of the studied species, we found heat stress-associated SNPs located in proximity of genes involved in pathways well known to contribute to the cellular responses against heat, such as protein folding, oxidative stress homeostasis, inflammatory and apoptotic pathways, and DNA damage-repair. In some cases, the same candidate molecular targets of heat stress adaptation recurred among species. Together, these results underline the relevance and the power of the seascape genomics approach for the discovery of adaptive traits that could allow corals to persist across wider thermal ranges.

Nested Species Distribution Models of Chlamydiales in Ixodes ricinus (Tick) Hosts in Switzerland.

The tick Ixodes ricinus is the vector of various pathogens, including Chlamydiales bacteria, which potentially cause respiratory infections. In this study, we modeled the spatial distribution of I. ricinus and associated Chlamydiales over Switzerland from 2009 to 2019. We used a total of 2,293 ticks and 186 Chlamydiales occurrences provided by a Swiss Army field campaign, a collaborative smartphone application, and a prospective campaign. For each tick location, we retrieved from Swiss federal data sets the environmental factors reflecting the topography, climate, and land cover. We then used the Maxent modeling technique to estimate the suitability of particular areas for I. ricinus and to subsequently build the nested niche of Chlamydiales bacteria. Results indicate that I. ricinus habitat suitability is determined by higher temperature and normalized difference vegetation index (NDVI) values, lower temperature during the driest months, and a higher percentage of artificial and forest areas. The performance of the model was improved when extracting the environmental variables for a 100-m radius buffer around the sampling points and when considering the climatic conditions of the 2 years previous to the sampling date. Chlamydiales bacteria were favored by a lower percentage of artificial surfaces, drier conditions, high precipitation during the coldest months, and short distances to wetlands. From 2009 to 2018, we observed an extension of areas suitable to ticks and Chlamydiales, associated with a shift toward higher altitude. The importance of considering spatiotemporal variations in the environmental conditions for obtaining better prediction was also demonstrated.IMPORTANCEIxodes ricinus is the vector of pathogens including the agent of Lyme disease, the tick-borne encephalitis virus, and the less well-known Chlamydiales bacteria, which are responsible for certain respiratory infections. In this study, we identified the environmental factors influencing the presence of I. ricinus and Chlamydiales in Switzerland and generated maps of their distribution from 2009 to 2018. We found an important expansion of suitable areas for both the tick and the bacteria during the last decade. Results also provided the environmental factors that determine the presence of Chlamydiales within ticks. Distribution maps as generated here are expected to bring valuable information for decision makers in controlling tick-borne diseases in Switzerland and establishing prevention campaigns. The methodological framework presented could be used to predict the distribution and spread of other host-pathogen pairs to identify environmental factors driving their distribution and to develop control or prevention strategies accordingly.

Signatures of selection and environmental adaptation across the goat genome post-domestication.

BACKGROUND: Since goat was domesticated 10,000 years ago, many factors have contributed to the differentiation of goat breeds and these are classified mainly into two types: (i) adaptation to different breeding systems and/or purposes and (ii) adaptation to different environments. As a result, approximately 600 goat breeds have developed worldwide; they differ considerably from one another in terms of phenotypic characteristics and are adapted to a wide range of climatic conditions. In this work, we analyzed the AdaptMap goat dataset, which is composed of data from more than 3000 animals collected worldwide and genotyped with the CaprineSNP50 BeadChip. These animals were partitioned into groups based on geographical area, production uses, available records on solid coat color and environmental variables including the sampling geographical coordinates, to investigate the role of natural and/or artificial selection in shaping the genome of goat breeds. RESULTS: Several signatures of selection on different chromosomal regions were detected across the different breeds, sub-geographical clusters, phenotypic and climatic groups. These regions contain genes that are involved in important biological processes, such as milk-, meat- or fiber-related production, coat color, glucose pathway, oxidative stress response, size, and circadian clock differences. Our results confirm previous findings in other species on adaptation to extreme environments and human purposes and provide new genes that could explain some of the differences between goat breeds according to their geographical distribution and adaptation to different environments. CONCLUSIONS: These analyses of signatures of selection provide a comprehensive first picture of the global domestication process and adaptation of goat breeds and highlight possible genes that may have contributed to the differentiation of this species worldwide.

Genome-wide SNP profiling of worldwide goat populations reveals strong partitioning of diversity and highlights post-domestication migration routes.

BACKGROUND: Goat populations that are characterized within the AdaptMap project cover a large part of the worldwide distribution of this species and provide the opportunity to assess their diversity at a global scale. We analysed genome-wide 50 K single nucleotide polymorphism (SNP) data from 144 populations to describe the global patterns of molecular variation, compare them to those observed in other livestock species, and identify the drivers that led to the current distribution of goats. RESULTS: A high degree of genetic variability exists among the goat populations studied. Our results highlight a strong partitioning of molecular diversity between and within continents. Three major gene pools correspond to goats from Europe, Africa and West Asia. Dissection of sub-structures disclosed regional gene pools, which reflect the main post-domestication migration routes. We also identified several exchanges, mainly in African populations, and which often involve admixed and cosmopolitan breeds. Extensive gene flow has taken place within specific areas (e.g., south Europe, Morocco and Mali-Burkina Faso-Nigeria), whereas elsewhere isolation due to geographical barriers (e.g., seas or mountains) or human management has decreased local gene flows. CONCLUSIONS: After domestication in the Fertile Crescent in the early Neolithic era (ca. 12,000 YBP), domestic goats that already carried differentiated gene pools spread to Europe, Africa and Asia. The spread of these populations determined the major genomic background of the continental populations, which currently have a more marked subdivision than that observed in other ruminant livestock species. Subsequently, further diversification occurred at the regional level due to geographical and reproductive isolation, which was accompanied by additional migrations and/or importations, the traces of which are still detectable today. The effects of breed formation were clearly detected, particularly in Central and North Europe. Overall, our results highlight a remarkable diversity that occurs at the global scale and is locally partitioned and often affected by introgression from cosmopolitan breeds. These findings support the importance of long-term preservation of goat diversity, and provide a useful framework for investigating adaptive introgression, directing genetic improvement and choosing breeding targets.

Public smoking ban and socioeconomic inequalities in smoking prevalence and cessation: a cross-sectional population-based study in Geneva, Switzerland (1995-2014).

INTRODUCTION: Smoking bans were suggested to reduce smoking prevalence and increase quit ratio but their equity impact remains unclear. We aimed to characterise the socioeconomic status (SES)-related inequalities in smoking prevalence and quit ratio before and after the implementation of a public smoking ban. METHODS: We included data from 17 544 participants in the population-based cross-sectional Bus Santé study in Geneva, Switzerland, between 1995 and 2014. We considered educational attainment (primary, secondary and tertiary) as a SES indicator. Outcomes were smoking prevalence (proportion of current smokers) and quit ratio (ex-smokers to ever-smokers ratio). We used segmented linear regression to assess the overall impact of smoking ban on outcome trends. We calculated the relative (RII) and slope (SII, absolute difference) indexes of inequality, quantifying disparities between educational groups in outcomes overall (1995-2014), before and after ban implementation (November 2009). RESULTS: Least educated participants displayed higher smoking prevalence (RII=2.04, P<0.001; SII=0.15, P<0.001) and lower quit ratio (RII=0.73, P<0.001; SII=-0.18, P<0.001). As in other studies, smoking ban implementation coincided with a temporary reduction of smoking prevalence (P=0.003) and increase in quit ratio (P=0.02), with a progressive return to preban levels. Inequalities increased (P<0.05) in relative terms for smoking prevalence (RIIbefore=1.84, P<0.001 and RIIafter=3.01, P<0.001) and absolute terms for both outcomes (smoking prevalence: SIIbefore=0.14, P<0.001 and SIIafter=0.19, P<0.001; quit ratio: SIIbefore=-0.15, P<0.001 and SIIafter=-0.27, P<0.001). CONCLUSIONS: Implementation of a public smoking ban coincided with a short-lived decrease in smoking prevalence and increase in quit ratio but also with a widening in SES inequalities in smoking-related outcomes.

Combining Genotype, Phenotype, and Environment to Infer Potential Candidate Genes.

Population genomic analysis can be an important tool in understanding local adaptation. Identification of potential adaptive loci in such analyses is usually based on the survey of a large genomic dataset in combination with environmental variables. Phenotypic data are less commonly incorporated into such studies, although combining a genome scan analysis with a phenotypic trait analysis can greatly improve the insights obtained from each analysis individually. Here, we aimed to identify loci potentially involved in adaptation to climate in 283 Loblolly pine (Pinus taeda) samples from throughout the species' range in the southeastern United States. We analyzed associations between phenotypic, molecular, and environmental variables from datasets of 3082 single nucleotide polymorphism (SNP) loci and 3 categories of phenotypic traits (gene expression, metabolites, and whole-plant traits). We found only 6 SNP loci that displayed potential signals of local adaptation. Five of the 6 identified SNPs are linked to gene expression traits for lignin development, and 1 is linked with whole-plant traits. We subsequently compared the 6 candidate genes with environmental variables and found a high correlation in only 3 of them (R2 > 0.2). Our study highlights the need for a combination of genotypes, phenotypes, and environmental variables, and for an appropriate sampling scheme and study design, to improve confidence in the identification of potential candidate genes.

Detecting spatial genetic signatures of local adaptation in heterogeneous landscapes.

The spatial structure of the environment (e.g. the configuration of habitat patches) may play an important role in determining the strength of local adaptation. However, previous studies of habitat heterogeneity and local adaptation have largely been limited to simple landscapes, which poorly represent the multiscale habitat structure common in nature. Here, we use simulations to pursue two goals: (i) we explore how landscape heterogeneity, dispersal ability and selection affect the strength of local adaptation, and (ii) we evaluate the performance of several genotype-environment association (GEA) methods for detecting loci involved in local adaptation. We found that the strength of local adaptation increased in spatially aggregated selection regimes, but remained strong in patchy landscapes when selection was moderate to strong. Weak selection resulted in weak local adaptation that was relatively unaffected by landscape heterogeneity. In general, the power of detection methods closely reflected levels of local adaptation. False-positive rates (FPRs), however, showed distinct differences across GEA methods based on levels of population structure. The univariate GEA approach had high FPRs (up to 55%) under limited dispersal scenarios, due to strong isolation by distance. By contrast, multivariate, ordination-based methods had uniformly low FPRs (0-2%), suggesting these approaches can effectively control for population structure. Specifically, constrained ordinations had the best balance of high detection and low FPRs and will be a useful addition to the GEA toolkit. Our results provide both theoretical and practical insights into the conditions that shape local adaptation and how these conditions impact our ability to detect selection.

Adaptations to climate-mediated selective pressures in sheep.

Following domestication, sheep (Ovis aries) have become essential farmed animals across the world through adaptation to a diverse range of environments and varied production systems. Climate-mediated selective pressure has shaped phenotypic variation and has left genetic "footprints" in the genome of breeds raised in different agroecological zones. Unlike numerous studies that have searched for evidence of selection using only population genetics data, here, we conducted an integrated coanalysis of environmental data with single nucleotide polymorphism (SNP) variation. By examining 49,034 SNPs from 32 old, autochthonous sheep breeds that are adapted to a spectrum of different regional climates, we identified 230 SNPs with evidence for selection that is likely due to climate-mediated pressure. Among them, 189 (82%) showed significant correlation (P ≤ 0.05) between allele frequency and climatic variables in a larger set of native populations from a worldwide range of geographic areas and climates. Gene ontology analysis of genes colocated with significant SNPs identified 17 candidates related to GTPase regulator and peptide receptor activities in the biological processes of energy metabolism and endocrine and autoimmune regulation. We also observed high linkage disequilibrium and significant extended haplotype homozygosity for the core haplotype TBC1D12-CH1 of TBC1D12. The global frequency distribution of the core haplotype and allele OAR22_18929579-A showed an apparent geographic pattern and significant (P ≤ 0.05) correlations with climatic variation. Our results imply that adaptations to local climates have shaped the spatial distribution of some variants that are candidates to underpin adaptive variation in sheep.

Investigation of the genetic diversity of domestic Capra hircus breeds reared within an early goat domestication area in Iran.

BACKGROUND: Iran is an area of particular interest for investigating goat diversity. Archaeological remains indicate early goat domestication (about 10,000 years ago) in the Iranian Zagros Mountains as well as in the high Euphrates valley and southeastern Anatolia. In addition, mitochondrial DNA data of domestic goats and wild ancestors (C. aegagrusor bezoar) suggest a pre-domestication management of wild populations in southern Zagros and central Iranian Plateau. In this study genetic diversity was assessed in seven Iranian native goat breeds, namely Markhoz, Najdi, Taleshi, Khalkhali, Naini, native Abadeh and Turki-Ghashghaei. A total of 317 animals were characterized using 14 microsatellite loci. Two Pakistani goat populations, Pahari and Teddy, were genotyped for comparison. RESULTS: Iranian goats possess a remarkable genetic diversity (average expected heterozygosity of 0.671 across loci, 10.7 alleles per locus) mainly accounted for by the within-breed component (GST = 5.9%). Positive and highly significant FIS values in the Naini, Turki-Ghashghaei, Abadeh and Markhoz breeds indicate some level of inbreeding in these populations. Multivariate analyses cluster Iranian goats into northern, central and western groups, with the western breeds relatively distinct from the others. Pakistani breeds show some relationship with Iranian populations, even if their position is not consistent across analyses. Gene flow was higher within regions (west, north, central) compared to between regions but particularly low between the western and the other two regions, probably due to the isolating topography of the Zagros mountain range. The Turki-Ghashghaei, Najdi and Abadeh breeds are reared in geographic areas where mtDNA provided evidence of early domestication. These breeds are highly variable, located on basal short branches in the neighbor-joining tree, close to the origin of the principal component analysis plot and, although highly admixed, they are quite distinct from those reared on the western side of the Zagros mountain range. CONCLUSIONS: These observations call for further investigation of the nuclear DNA diversity of these breeds within a much wider geographic context to confirm or re-discuss the current hypothesis (based on maternal lineage data) of an almost exclusive contribution of the eastern Anatolian bezoar to the domestic goat gene pool.

Environmental and geographical factors influencing the spread of SARS-CoV-2 over 2 years: a fine-scale spatiotemporal analysis.

Introduction: Since its emergence in late 2019, the SARS-CoV-2 virus has led to a global health crisis, affecting millions and reshaping societies and economies worldwide. Investigating the determinants of SARS-CoV-2 diffusion and their spatiotemporal dynamics at high spatial resolution is critical for public health and policymaking. Methods: This study analyses 194,682 georeferenced SARS-CoV-2 RT-PCR tests from March 2020 and April 2022 in the canton of Vaud, Switzerland. We characterized five distinct pandemic periods using metrics of spatial and temporal clustering like inverse Shannon entropy, the Hoover index, Lloyd's index of mean crowding, and the modified space-time DBSCAN algorithm. We assessed the demographic, socioeconomic, and environmental factors contributing to cluster persistence during each period using eXtreme Gradient Boosting (XGBoost) and SHapley Additive exPlanations (SHAP), to consider non-linear and spatial effects. Results: Our findings reveal important variations in the spatial and temporal clustering of cases. Notably, areas with flatter epidemics had higher total attack rate. Air pollution emerged as a factor showing a consistent positive association with higher cluster persistence, substantiated by both immission models and, to a lesser extent, tropospheric NO2 estimations. Factors including population density, testing rates, and geographical coordinates, also showed important positive associations with higher cluster persistence. The socioeconomic index showed no significant contribution to cluster persistence, suggesting its limited role in the observed dynamics, which warrants further research. Discussion: Overall, the determinants of cluster persistence remained across the study periods. These findings highlight the need for effective air quality management strategies to mitigate air pollution's adverse impacts on public health, particularly in the context of respiratory viral diseases like COVID-19.

Integrating very high resolution environmental proxies in genotype-environment association studies.

Landscape genomic analyses associating genetic variation with environmental variables are powerful tools for studying molecular signatures of species' local adaptation and for detecting candidate genes under selection. The development of landscape genomics over the past decade has been spurred by improvements in resolutions of genomic and environmental datasets, allegedly increasing the power to identify putative genes underlying local adaptation in non-model organisms. Although these associations have been successfully applied to numerous species across a diverse array of taxa, the spatial scale of environmental predictor variables has been largely overlooked, potentially limiting conclusions to be reached with these methods. To address this knowledge gap, we systematically evaluated performances of genotype-environment association (GEA) models using predictor variables at multiple spatial resolutions. Specifically, we used multivariate redundancy analyses to associate whole-genome sequence data from the plant Arabis alpina L. collected across four neighboring valleys in the western Swiss Alps, with very high-resolution topographic variables derived from digital elevation models of grain sizes between 0.5 m and 16 m. These comparisons highlight the sensitivity of landscape genomic models to spatial resolution, where the optimal grain sizes were specific to variable type, terrain characteristics, and study extent. To assist in selecting variables at appropriate spatial resolutions, we demonstrate a practical approach to produce, select, and integrate multiscale variables into GEA models. After generalizing fine-grained variables to multiple spatial resolutions, a forward selection procedure is applied to retain only the most relevant variables for a particular context. Depending on the spatial resolution, the relevance for topographic variables in GEA studies calls for integrating multiple spatial scales into landscape genomic models. By carefully considering spatial resolutions, candidate genes under selection by a more realistic range of pressures can be detected for downstream analyses, with important applied implications for experimental research and conservation management of natural populations.

Spatial analysis of 10-year predicted risk and incident atherosclerotic cardiovascular disease: the CoLaus cohort.

Whether cardiovascular risk scores geographically aggregate and inform on spatial development of atherosclerotic cardiovascular diseases (ASCVD) remains unknown. Our aim is to determine the spatial distribution of 10-year predicted cardiovascular risk and ASCVD, and to compare the overlap of the resulting spatial distributions. Using prospective data from the CoLaus|PsyCoLaus cohort study (2003-2021) we computed SCORE2 in participants free from ASCVD. Geographical distributions of predicted risk and events were determined using the Gi* Getis-Ord autocorrelation statistic. 6203 individuals (54% women, mean age 52.5 ± SD 10.7, ASCVD incidence rate 5.7%) were included. We identified clusters of high versus low predicted risk (4%, 6%, respectively) and ASCVD (5%, 5% respectively) at baseline. They persisted at follow-up. Overlap of SCORE2 and ASCVD clusters was marginal. Body-mass index and alcohol consumption explained most of the predicted risk distribution. For ASCVD, high clusters persisted or were reinforced after multivariate adjustment, while low incidence clusters were reduced, multifactorial determinants. Incidence rate of ASCVD was 2.5% higher (IC 95%, 1.4-3.7) in clusters of higher incidence of ASCVD. To develop up-to-date, geographically targeted prevention strategies, there is a need to study novel geographically risk factors affecting ASCVD and to update commonly used prediction models for a population approach.

Meeting review. Uncovering the genetic basis of adaptive change: on the intersection of landscape genomics and theoretical population genetics.

A workshop recently held at the École Polytechnique Fédérale de Lausanne (EPFL, Switzerland) was dedicated to understanding the genetic basis of adaptive change, taking stock of the different approaches developed in theoretical population genetics and landscape genomics and bringing together knowledge accumulated in both research fields. Indeed, an important challenge in theoretical population genetics is to incorporate effects of demographic history and population structure. But important design problems (e.g. focus on populations as units, focus on hard selective sweeps, no hypothesis-based framework in the design of the statistical tests) reduce their capability of detecting adaptive genetic variation. In parallel, landscape genomics offers a solution to several of these problems and provides a number of advantages (e.g. fast computation, landscape heterogeneity integration). But the approach makes several implicit assumptions that should be carefully considered (e.g. selection has had enough time to create a functional relationship between the allele distribution and the environmental variable, or this functional relationship is assumed to be constant). To address the respective strengths and weaknesses mentioned above, the workshop brought together a panel of experts from both disciplines to present their work and discuss the relevance of combining these approaches, possibly resulting in a joint software solution in the future.

Measuring Nurses' Knowledge and Awareness of Climate Change and Climate-Associated Diseases: Protocol for a Systematic Review of Existing Instruments.

BACKGROUND: Climate change is a health emergency. Each year, it is estimated to cost more than 230 million years of life expectancy, with 4-9 million premature deaths associated with air pollution, and 9 million excess deaths due to non-optimal temperatures, representing 7% more temperature-related deaths since 2015 and 66% more since 2000. OBJECTIVE: Identify and evaluate the reliability, fidelity, and validity of instruments measuring nurses' knowledge and awareness of climate change and climate-associated diseases. METHODS: A systematic literature review will retrieve and assess studies examining instruments measuring nurses' knowledge and awareness of climate change and climate-associated diseases. Using predefined search terms for nurses, climate change, literacy and scales or tools, we will search for published articles recorded in the following electronic databases, with no language or date restrictions, from their inception until 31 October 2023: Medline Ovid SP (from 1946), PubMed (NOT Medline[sb], from 1996), Embase.com (from 1947), CINAHL Ebesco (from 1937), the Cochrane Library Wiley (from 1992), Web of Science Core Collection (from 1900), the Trip Database (from 1997), JBI OVID SP (from 1998), and the GreenFILE EBSCO. We will also hand-search relevant articles' bibliographies and search for unpublished studies using Google Scholar, ProQuest Dissertations and Theses Global, and DART-EUrope.eu. This will be completed by exploring the gray literature in OpenGrey and the Grey Literature Report, from inception until 31 October 2023, in collaboration with a librarian. Twelve bibliographic databases will be searched for publications up to 31 October 2023. The papers selected will be assessed for their quality. RESULTS: The electronic database searches were completed in May 2023. Retrieved articles are being screened, and the study will be completed by October 2023. After removing duplicates, our search strategy has retrieved 3449 references. CONCLUSIONS: This systematic review will provide specific knowledge about instruments to measure nurses' knowledge, awareness, motivation, attitudes, behaviors, beliefs, skills, and competencies regarding climate change and climate-associated diseases.

Climate Change, Environmental Health, and Challenges for Nursing Discipline.

Current data and scientific predictions about the consequences of climate change are accurate in suggesting disaster. Since 2019, climate change has become a threat to human health, and major consequences on health and health systems are already observed. Climate change is a central concern for the nursing discipline, even though nursing theorists' understanding of the environment has led to problematic gaps that impact the current context. Today, nursing discipline is facing new challenges. Nurses are strategically placed to respond to the impacts of climate change through their practice, research, and training in developing, implementing, and sustaining innovation towards climate change mitigation and adaptation. It is urgent for them to adapt their practice to this reality to become agents of change.

Small area vulnerability, household food insecurity and child malnutrition in Medellin, Colombia: results from a repeated cross-sectional study.

Background: Malnutrition and food insecurity might be driven not only by individual factors but also by contextual conditions, such as area-level deprivation or vulnerability. This study aimed to analyze the association between area-level vulnerability and i) household food insecurity and ii) malnutrition in children in Medellin, Colombia, during the years 2017 and 2018. Methods: We obtained data from two different sources: the Living Standards Measurement Survey (LSMS) and the nutrition surveillance system of Medellin. The main outcomes were food insecurity in households with children and anthropometric indicators for children under five. The main predictor was area-level vulnerability. Mixed effects Poisson regression with robust standard errors models were conducted to test the association of quintiles of deprivation with each outcome. Findings: Households with children living in areas with the highest deprivation had 1.9 times the prevalence of food insecurity as compared to those living in areas with the lowest deprivation (PR 1.91, 95% CI 1.42-2.57). Similar results were observed for underweight/risk of underweight (PR 1.26, 95% CI 1.11-1.42), stunting/risk of stunting (PR 1.36, 95% CI 1.22-1.53) and stunting (PR 1.93 95% CI 1.55-2.39) among children under five. We found no consistent associations with wasting/risk of wasting or excess weight/risk of overweight across quintiles of deprivation. Interpretation: This study sheds light on the role of area-level vulnerability on malnutrition in children in Medellin, Colombia, showing a pattern of increasing prevalence of food insecurity, underweight and stunting by quintile of deprivation. Funding: Swiss School of Public Health (SSPH+) and Centre for Global Health Inequalities Research (CHAIN).

Vertical distribution and seasonal dynamics of planktonic cyanobacteria communities in a water column of deep mesotrophic Lake Geneva.

Background: Temperate subalpine lakes recovering from eutrophication in central Europe are experiencing harmful blooms due to the proliferation of Planktothrix rubescens, a potentially toxic cyanobacteria. To optimize the management of cyanobacteria blooms there is the need to better comprehend the combination of factors influencing the diversity and dominance of cyanobacteria and their impact on the lake's ecology. The goal of this study was to characterize the diversity and seasonal dynamics of cyanobacteria communities found in a water column of Lake Geneva, as well as the associated changes on bacterioplankton abundance and composition. Methods: We used 16S rRNA amplicon high throughput sequencing on more than 200 water samples collected from surface to 100 meters deep monthly over 18 months. Bacterioplankton abundance was determined by quantitative PCR and PICRUSt predictions were used to explore the functional pathways present in the community and to calculate functional diversity indices. Results: The obtained results confirmed that the most dominant cyanobacteria in Lake Geneva during autumn and winter was Planktothrix (corresponding to P. rubescens). Our data also showed an unexpectedly high relative abundance of picocyanobacterial genus Cyanobium, particularly during summertime. Multidimensional scaling of Bray Curtis dissimilarity revealed that the dominance of P. rubescens was coincident with a shift in the bacterioplankton community composition and a significant decline in bacterioplankton abundance, as well as a temporary reduction in the taxonomic and PICRUSt2 predicted functional diversity. Conclusion: Overall, this study expands our fundamental understanding of the seasonal dynamics of cyanobacteria communities along a vertical column in Lake Geneva and the ecology of P. rubescens, ultimately contributing to improve our preparedness against the potential occurrence of toxic blooms in the largest lake of western Europe.

Geographic clusters of objectively measured physical activity and the characteristics of their built environment in a Swiss urban area.

INTRODUCTION: Evidence suggests that the built environment can influence the intensity of physical activity. However, despite the importance of the geographic context, most of the studies do not consider the spatial framework of this association. We aimed to assess individual spatial dependence of objectively measured moderate and vigorous physical activity (MVPA) and describe the characteristics of the built environment among spatial clusters of MVPA. METHODS: Cross-sectional data from the second follow-up (2014-2017) of CoLaus|PsyCoLaus, a longitudinal population-based study of the Lausanne area (Switzerland), was used to objectively measure MVPA using accelerometers. Local Moran's I was used to assess the spatial dependence of MVPA and detect geographic clusters of low and high MVPA. Additionally, the characteristics of the built environment observed in the clusters based on raw MVPA and MVPA adjusted for socioeconomic and demographic factors were compared. RESULTS: Data from 1,889 participants (median age 63, 55% women) were used. The geographic distribution of MVPA and the characteristics of the built environment among clusters were similar for raw and adjusted MVPA. In the adjusted model, we found a low concentration of individuals within spatial clusters of high MVPA (median: 38.5mins; 3% of the studied population) and low MVPA (median: 10.9 mins; 2% of the studied population). Yet, clear differences were found in both models between clusters regarding the built environment; high MVPA clusters were located in areas where specific compositions of the built environment favor physical activity. CONCLUSIONS: Our results suggest the built environment may influence local spatial patterns of MVPA independently of socioeconomic and demographic factors. Interventions in the built environment should be considered to promote physically active behaviors in urban areas.