In a time of global change, having an understanding of the nature of biotic and abiotic factors that drive a species’ range may be the sharpest tool in the arsenal of conservation and management of threatened species. However, such information is lacking for most tropical and epiphytic species due to the complexity of life history, the roles of stochastic events, and the diversity of habitat across the span of a distribution. In this study, we conducted repeated censuses across the core and peripheral range of Trichocentrum undulatum, a threatened orchid that is found throughout the island of Cuba (species core range) and southern Florida (the northern peripheral range). We used demographic matrix modeling as well as stochastic simulations to investigate the impacts of herbivory, hurricanes, and logging (in Cuba) on projected population growth rates (? and ?s) among sites. Methods Field methods Censuses took place between 2013 and 2021. The longest census period was that of the Peripheral population with a total of nine years (2013–2021). All four populations in Cuba used in demographic modeling that were censused more than once: Core 1 site (2016–2019, four years), Core 2 site (2018–2019, two years), Core 3 (2016 and 2018 two years), and Core 4 (2018–2019, two years) (Appendix S1: Table S1). In November 2017, Hurricane Irma hit parts of Cuba and southern Florida, impacting the Peripheral population. The Core 5 population (censused on 2016 and 2018) was small (N=17) with low survival on the second census due to logging. Three additional populations in Cuba were visited only once, Core 6, Core 7, and Core 8 (Table 1). Sites with one census or with a small sample size (Core 5) were not included in the life history and matrix model analyses of this paper due to the lack of population transition information, but they were included in the analysis on the correlation between herbivory and fruit rate, as well as the use of mortality observations from logging for modeling. All Cuban sites were located between Western and Central Cuba, spanning four provinces: Mayabeque (Core 1), Pinar del Rio (Core 2 and Core 6), Matanzas (Core 3 and Core 5), and Sancti Spiritus (Core 4, Core 7, Core 8). At each population of T. undulatum presented in this study, individuals were studied within ~1-km strips where T. undulatum occurrence was deemed representative of the site, mostly occurring along informal forest trails. Once an individual of T. undulatum was located, all trees within a 5-m radius were searched for additional individuals. Since tagging was not permitted, we used a combination of information to track individual plants for the repeated censuses. These include the host species, height of the orchid, DBH of the host tree, and hand-drawn maps. Individual plants were also marked by GPS at the Everglades Peripheral site. If a host tree was found bearing more than one T. undulatum, then we systematically recorded the orchids in order from the lowest to highest as well as used the previous years’ observations in future censuses for individualized notes and size records. We recorded plant size and reproductive variables during each census including: the number of leaves, length of the longest leaf (cm), number of inflorescence stalks, number of flowers, and the number of mature fruits. We also noted any presence of herbivory, such as signs of being bored by M. miamensis, and whether an inflorescence was partially or completely affected by the fly, and whether there was other herbivory, such as D. boisduvalii on leaves. We used logistic regression analysis to examine the effects of year (at the Peripheral site) and sites (all sites) on the presence or absence of inflorescence herbivory at all the sites. Cross tabulation and chi-square analysis were done to examine the associations between whether a plant was able to fruit and the presence of floral herbivory by M. miamensis. The herbivory was scored as either complete or partial. During the orchid population scouting expeditions, we came across a small population in the Matanzas province (Core 5, within 10 km of the Core 3 site) and recorded the demographic information. Although the sampled population was small (N = 17), we were able to observe logging impacts at the site and recorded logging-associated mortality on the subsequent return to the site. Matrix modeling Definition of size-stage classes To assess the life stage transitions and population structures for each plant for each population’s census period we first defined the stage classes for the species. The categorization for each plant’s stage class depended on both its size and reproductive capabilities, a method deemed appropriate for plants (Lefkovitch 1965, Cochran and Ellner 1992). A size index score was calculated for each plant by taking the total number of observed leaves and adding the length of the longest leaf, an indication of accumulated biomass (Borrero et al. 2016). The smallest plant size that attempted to produce an inflorescence is considered the minimum size for an adult plant. Plants were classified by stage based on their size index and flowering capacity as the following: (1) seedlings (or new recruits), i.e., new and small plants with a size index score of less than 6, (2) juveniles, i.e., plants with a size index score of less than 15 with no observed history of flowering, (3) adults, plants with size index scores of 15 or greater. Adult plants of this size or larger are capable of flowering but may not produce an inflorescence in a given year. The orchid’s population matrix models were constructed based on these stages. In general, orchid seedlings are notoriously difficult to observe and easily overlooked in the field due to the small size of protocorms. A newly found juvenile on a subsequent site visit (not the first year) may therefore be considered having previously been a seedling in the preceding year. In this study, we use the discovered “seedlings” as indicatory of recruitment for the populations. Adult plants are able to shrink or transition into the smaller juvenile stage class, but a juvenile cannot shrink to the seedling stage. Matrix elements and population vital rates calculations Annual transition probabilities for every stage class were calculated. A total of 16 site- and year-specific matrices were constructed. When seedling or juvenile sample sizes were < 9, the transitions were estimated using the nearest year or site matrix elements as a proxy. Due to the length of the study and variety of vegetation types with a generally large population size at each site, transition substitutions were made with the average stage transition from all years at the site as priors. If the sample size of the averaged stage was still too small, the averaged transition from a different population located at the same vegetation type was used. We avoided using transition values from populations found in different vegetation types to conserve potential environmental differences. A total of 20% (27/135) of the matrix elements were estimated in this fashion, the majority being seedling stage transitions (19/27) and noted in the Appendices alongside population size (Appendix S1: Table S1). The fertility element transitions from reproductive adults to seedlings were calculated as the number of seedlings produced (and that survived to the census) per adult plant. Deterministic modeling analysis We used integral projection models (IPM) to project the long-term population growth rates for each time period and population. The finite population growth rate (?), stochastic long-term growth rate (?s), and the elasticity were projected for each matrices using R Popbio Package 2.4.4 (Stubben and Milligan 2007, Caswell 2001). The elasticity matrices were summarized by placing each element into one of three categories: fecundity (transition from reproductive adults to seedling stage), growth (all transitions to new and more advanced stage, excluding the fecundity), and stasis (plants that transitioned into the same or a less advanced stage on subsequent census) (Liu et al. 2005). Life table response experiments (LTREs) were conducted to identify the stage transitions that had the greatest effects on observed differences in population growth between select sites and years (i.e., pre-post hurricane impact and site comparisons of same vegetation type). Due to the frequent disturbances that epiphytes in general experience as well as our species’ distribution in hurricane-prone areas, we ran transient dynamic models that assume that the populations censused were not at stable stage distributions (Stott et al. 2011). We calculated three indices for short-term transient dynamics to capture the variation during a 15-year transition period: reactivity, maximum amplification, and amplified inertia. Reactivity measures a population’s growth in a single measured timestep relative to the stable-stage growth, during the simulated transition period. Maximum amplification and amplified inertia are the maximum of future population density and the maximum long-term population density, respectively, relative to a stable-stage population that began at the same initial density (Stott et al. 2011). For these analyses, we used a mean matrix for Core 1, Core 2 Core 3, and Core 4 sites and the population structure of their last census. For the Peripheral site, we averaged the last three matrices post-hurricane disturbance and used the most-recent population structure. We standardized the indices across sites with the assumption of initial population density equal to 1 (Stott et al. 2011). Analysis was done using R Popdemo version 1.3-0 (Stott et al. 2012b). Stochastic simulation We created matrices to simulate the effects of episodic recruitment, hurricane impacts, herbivory, and logging (Appendix S1: Table S2). The Peripheral population is the longest-running site with nine years of censuses (eight

Throughout most of human history, global population growth was very low; between 10,000BCE and 1700CE, the average annual increase was just 0.04 percent. Therefore, it took several thousand years for the global population to reach one billion people, doing so in 1803. However, this period marked the beginning of a global phenomenon known as the demographic transition, from which point population growth skyrocketed. With the introduction of modern medicines (especially vaccination), as well as improvements in water sanitation, food supply, and infrastructure, child mortality fell drastically and life expectancy increased, causing the population to grow. This process is linked to economic and technological development, and did not take place concurrently across the globe; it mostly began in Europe and other industrialized regions in the 19thcentury, before spreading across Asia and Latin America in the 20th century. As the most populous societies in the world are found in Asia, the demographic transition in this region coincided with the fastest period of global population growth. Today, Sub-Saharan Africa is the region at the earliest stage of this transition. As population growth slows across the other continents, with the populations of the Americas, Asia, and Europe expected to be in decline by the 2070s, Africa's population is expected to grow by three billion people by the end of the 21st century.

In 2025, there are six countries, all in Sub-Saharan Africa, where the average woman of childbearing age can expect to have between 5-6 children throughout their lifetime. In fact, of the 20 countries in the world with the highest fertility rates, Afghanistan and Yemen are the only countries not found in Sub-Saharan Africa. High fertility rates in Africa With a fertility rate of almost six children per woman, Chad is the country with the highest fertility rate in the world. Population growth in Chad is among the highest in the world. Lack of healthcare access, as well as food instability, political instability, and climate change, are all exacerbating conditions that keep Chad's infant mortality rates high, which is generally the driver behind high fertility rates. This situation is common across much of the continent, and, although there has been considerable progress in recent decades, development in Sub-Saharan Africa is not moving as quickly as it did in other regions. Demographic transition While these countries have the highest fertility rates in the world, their rates are all on a generally downward trajectory due to a phenomenon known as the demographic transition. The third stage (of five) of this transition sees birth rates drop in response to decreased infant and child mortality, as families no longer feel the need to compensate for lost children. Eventually, fertility rates fall below replacement level (approximately 2.1 children per woman), which eventually leads to natural population decline once life expectancy plateaus. In some of the most developed countries today, low fertility rates are creating severe econoic and societal challenges as workforces are shrinking while aging populations are placin a greater burden on both public and personal resources.

Environmental change continually perturbs populations from a stable state, leading to transient dynamics that can last multiple generations. Several long-term studies have reported changes in trait distributions along with demographic response to environmental change. Here we conducted an experimental study on soil mites and investigated the interaction between demography and an individual trait over a period of nonstationary dynamics. By following individual fates and body sizes at each life-history stage, we investigated how body size and population density influenced demographic rates. By comparing the ability of two alternative approaches, a matrix projection model and an integral projection model, we investigated whether consideration of trait-based demography enhances our ability to predict transient dynamics. By utilizing a prospective perturbation analysis, we addressed which stage-specific demographic or trait-transition rate had the greatest influence on population dynamics. Both body size and population density had important effects on most rates; however, these effects differed substantially among life-history stages. Considering the observed trait-demography relationships resulted in better predictions of a population’s response to perturbations, which highlights the role of phenotypic plasticity in transient dynamics. Although the perturbation analyses provided comparable predictions of stage-specific elasticities between the matrix and integral projection models, the order of importance of the life-history stages differed between the two analyses. In conclusion, we demonstrate how a trait-based demographic approach provides further insight into transient population dynamics. Daily sampling of individual mitesday: day of the study (day t) | no: individual ID for each day | surv: survival to day t+1? | stage: life-history stage at day t | stage1: life-history stage at day t+1 | trns: transition to next stage at day t+1? | tsex: transition to female stage at day t+1? | dens: weighted population density at day t | size: log(body size) at day t | size1: log(body size) at day t+1 | rep: produced eggs at day t+1? | rec: number of eggs produced on day t+1 | day2: number of eggs hatched on day t+2 | day3: number of eggs hatched on day t+3 | day4: number of eggs hatched on day t+4 | day5: number of eggs hatched on day t+5 | day6: number of eggs hatched on day t+6 | day7: number of eggs hatched after day t+6 | eggsurv: proportion of eggs hatched | hrate: daily hatching rate | eggsize: average log(egg size)ind_data.csvAdditional experiment measuring egg-to-larva size transitioneggSize: log(egg size) | larvaSize: log(larva size)egg_data.csvDaily population censusday: day of the study (day t) | e: number of eggs | l: number of larvae | p: number of protonymphs | t: number of tritonymphs | f: number of female adults | m: number of male adults | group: (c)ontrol or (s)ample group? | dens: weighted population densitypop_census.csv

AbstractConserving species requires knowledge of demographic rates (survival, recruitment) that govern population dynamics to allow the allocation of limited resources to the most vulnerable stages of target species' life cycles. Additionally, quantifying drivers of demographic change facilitates the enactment of specific remediation strategies. However, knowledge gaps persist in how similar environmental changes lead to contrasting population dynamics through demographic rates. For sympatric hummingbird species, the population of urban-associated partial-migrant Anna's hummigbird (Calypte anna) has increased, yet the populations of Neotropical migrants including rufous, calliope, and black-chinned hummingbirds have decreased. Here, we developed an integrated population model to jointly analyze 25 years of mark-recapture data and population survey data for these four species. We examined the contributions of demographic rates on population growth and evaluated the effects of anthropogenic stressors including human population density and crop cover on demographic change in relation to species' life histories. While recruitment appeared to drive the population increase of urban-associated Anna's hummingbirds, decreases in juvenile survival contributed most strongly to population declines of Neotropical migrants and highlight a potentially vulnerable phase in their life-history. Moreover, rufous hummingbird adult and juvenile survival rates were negatively impacted by human population density. Mitigating threats associated with intensively modified anthropogenic environments is a promising avenue for slowing further hummingbird population loss. Overall, our model grants critical insight into how anthropogenic modification of habitat affects the population dynamics of species of conservation concern. MethodsThis R data file contains a named list for each species in our study. It has been processed to remove covariates and data that are not public domain but are available for download at the links provided (indicated with * in the readme file). Each species list contains mark-recapture records (y), the known-state records (z), number of years spanned by the analysis (n.years), numbers banded individuals (n.ind), banding station membership (sta), number of banding stations (n.sta), year of first encounter for each individual (first), year of last possible encounter of each individual if it were to be alive (last), first and last years of mark recapture data (first_yr / last_yr), sex (1 = male, 2 = female) and age (1 = juvenile, 2 = adult) membership for each individual, the observed residency information for each individual in each year (r), the partially observed residency state information for each individual (u), the standardized human population density and crop data in the 3 kilometers around each banding station (HPD / crop), the unstandardized HPD and crop data (HPD_raw / crop_raw), the number of days of operational banding activity at each station each year (effort), and indicator for each station and year signifying whether banding occurred on at least two occasions separated by more than 5 days that year (kappa_shrink), the BBS survey year (year), an indicator of whether the BBS surveyor was suveying on their first year or not (firstyr), the number of BBS surveys (ncounts), the species tally on a given survey (count), the number of individual transects surveyed over the study period (nrte), the BBS transect membership for each count (rte), the number of observers contributing data over the study period (nobserver), the anonymized observer ID on a given transect for each count (rte.obser), and the initial abundance estimate given as the mean count across all transects and years, inflated by 100 for precise estimation of demographic rates (lam0). Usage notesData can be opened in R and analyzed using Nimble.

In 2022, India overtook China as the world's most populous country and now has almost 1.46 billion people. China now has the second-largest population in the world, still with just over 1.4 billion inhabitants, however, its population went into decline in 2023. Global population As of 2025, the world's population stands at almost 8.2 billion people and is expected to reach around 10.3 billion people in the 2080s, when it will then go into decline. Due to improved healthcare, sanitation, and general living conditions, the global population continues to increase; mortality rates (particularly among infants and children) are decreasing and the median age of the world population has steadily increased for decades. As for the average life expectancy in industrial and developing countries, the gap has narrowed significantly since the mid-20th century. Asia is the most populous continent on Earth; 11 of the 20 largest countries are located there. It leads the ranking of the global population by continent by far, reporting four times as many inhabitants as Africa. The Demographic Transition The population explosion over the past two centuries is part of a phenomenon known as the demographic transition. Simply put, this transition results from a drastic reduction in mortality, which then leads to a reduction in fertility, and increase in life expectancy; this interim period where death rates are low and birth rates are high is where this population explosion occurs, and population growth can remain high as the population ages. In today's most-developed countries, the transition generally began with industrialization in the 1800s, and growth has now stabilized as birth and mortality rates have re-balanced. Across less-developed countries, the stage of this transition varies; for example, China is at a later stage than India, which accounts for the change in which country is more populous - understanding the demographic transition can help understand the reason why China's population is now going into decline. The least-developed region is Sub-Saharan Africa, where fertility rates remain close to pre-industrial levels in some countries. As these countries transition, they will undergo significant rates of population growth

Today, globally, women of childbearing age have an average of approximately 2.2 children over the course of their lifetime. In pre-industrial times, most women could expect to have somewhere between five and ten live births throughout their lifetime; however, the demographic transition then sees fertility rates fall significantly. Looking ahead, it is believed that the global fertility rate will fall below replacement level in the 2050s, which will eventually lead to population decline when life expectancy plateaus. Recent decades Between the 1950s and 1970s, the global fertility rate was roughly five children per woman - this was partly due to the post-WWII baby boom in many countries, on top of already-high rates in less-developed countries. The drop around 1960 can be attributed to China's "Great Leap Forward", where famine and disease in the world's most populous country saw the global fertility rate drop by roughly 0.5 children per woman. Between the 1970s and today, fertility rates fell consistently, although the rate of decline noticeably slowed as the baby boomer generation then began having their own children. Replacement level fertility Replacement level fertility, i.e. the number of children born per woman that a population needs for long-term stability, is approximately 2.1 children per woman. Populations may continue to grow naturally despite below-replacement level fertility, due to reduced mortality and increased life expectancy, however, these will plateau with time and then population decline will occur. It is believed that the global fertility rate will drop below replacement level in the mid-2050s, although improvements in healthcare and living standards will see population growth continue into the 2080s when the global population will then start falling.

COVID-19 Trends MethodologyOur goal is to analyze and present daily updates in the form of recent trends within countries, states, or counties during the COVID-19 global pandemic. The data we are analyzing is taken directly from the Johns Hopkins University Coronavirus COVID-19 Global Cases Dashboard, though we expect to be one day behind the dashboard’s live feeds to allow for quality assurance of the data.Revisions added on 4/23/2020 are highlighted.Revisions added on 4/30/2020 are highlighted.Discussion of our assertion of an abundance of caution in assigning trends in rural counties added 5/7/2020. Correction on 6/1/2020Methodology update on 6/2/2020: This sets the length of the tail of new cases to 6 to a maximum of 14 days, rather than 21 days as determined by the last 1/3 of cases. This was done to align trends and criteria for them with U.S. CDC guidance. The impact is areas transition into Controlled trend sooner for not bearing the burden of new case 15-21 days earlier.Reasons for undertaking this work:The popular online maps and dashboards show counts of confirmed cases, deaths, and recoveries by country or administrative sub-region. Comparing the counts of one country to another can only provide a basis for comparison during the initial stages of the outbreak when counts were low and the number of local outbreaks in each country was low. By late March 2020, countries with small populations were being left out of the mainstream news because it was not easy to recognize they had high per capita rates of cases (Switzerland, Luxembourg, Iceland, etc.). Additionally, comparing countries that have had confirmed COVID-19 cases for high numbers of days to countries where the outbreak occurred recently is also a poor basis for comparison.The graphs of confirmed cases and daily increases in cases were fit into a standard size rectangle, though the Y-axis for one country had a maximum value of 50, and for another country 100,000, which potentially misled people interpreting the slope of the curve. Such misleading circumstances affected comparing large population countries to small population counties or countries with low numbers of cases to China which had a large count of cases in the early part of the outbreak. These challenges for interpreting and comparing these graphs represent work each reader must do based on their experience and ability. Thus, we felt it would be a service to attempt to automate the thought process experts would use when visually analyzing these graphs, particularly the most recent tail of the graph, and provide readers with an a resulting synthesis to characterize the state of the pandemic in that country, state, or county.The lack of reliable data for confirmed recoveries and therefore active cases. Merely subtracting deaths from total cases to arrive at this figure progressively loses accuracy after two weeks. The reason is 81% of cases recover after experiencing mild symptoms in 10 to 14 days. Severe cases are 14% and last 15-30 days (based on average days with symptoms of 11 when admitted to hospital plus 12 days median stay, and plus of one week to include a full range of severely affected people who recover). Critical cases are 5% and last 31-56 days. Sources:U.S. CDC. April 3, 2020 Interim Clinical Guidance for Management of Patients with Confirmed Coronavirus Disease (COVID-19). Accessed online. Initial older guidance was also obtained online. Additionally, many people who recover may not be tested, and many who are, may not be tracked due to privacy laws. Thus, the formula used to compute an estimate of active cases is: Active Cases = 100% of new cases in past 14 days + 19% from past 15-30 days + 5% from past 31-56 days - total deaths.We’ve never been inside a pandemic with the ability to learn of new cases as they are confirmed anywhere in the world. After reviewing epidemiological and pandemic scientific literature, three needs arose. We need to specify which portions of the pandemic lifecycle this map cover. The World Health Organization (WHO) specifies six phases. The source data for this map begins just after the beginning of Phase 5: human to human spread and encompasses Phase 6: pandemic phase. Phase six is only characterized in terms of pre- and post-peak. However, these two phases are after-the-fact analyses and cannot ascertained during the event. Instead, we describe (below) a series of five trends for Phase 6 of the COVID-19 pandemic.Choosing terms to describe the five trends was informed by the scientific literature, particularly the use of epidemic, which signifies uncontrolled spread. The five trends are: Emergent, Spreading, Epidemic, Controlled, and End Stage. Not every locale will experience all five, but all will experience at least three: emergent, controlled, and end stage.This layer presents the current trends for the COVID-19 pandemic by country (or appropriate level). There are five trends:Emergent: Early stages of outbreak. Spreading: Early stages and depending on an administrative area’s capacity, this may represent a manageable rate of spread. Epidemic: Uncontrolled spread. Controlled: Very low levels of new casesEnd Stage: No New cases These trends can be applied at several levels of administration: Local: Ex., City, District or County – a.k.a. Admin level 2State: Ex., State or Province – a.k.a. Admin level 1National: Country – a.k.a. Admin level 0Recommend that at least 100,000 persons be represented by a unit; granted this may not be possible, and then the case rate per 100,000 will become more important.Key Concepts and Basis for Methodology: 10 Total Cases minimum threshold: Empirically, there must be enough cases to constitute an outbreak. Ideally, this would be 5.0 per 100,000, but not every area has a population of 100,000 or more. Ten, or fewer, cases are also relatively less difficult to track and trace to sources. 21 Days of Cases minimum threshold: Empirically based on COVID-19 and would need to be adjusted for any other event. 21 days is also the minimum threshold for analyzing the “tail” of the new cases curve, providing seven cases as the basis for a likely trend (note that 21 days in the tail is preferred). This is the minimum needed to encompass the onset and duration of a normal case (5-7 days plus 10-14 days). Specifically, a median of 5.1 days incubation time, and 11.2 days for 97.5% of cases to incubate. This is also driven by pressure to understand trends and could easily be adjusted to 28 days. Source used as basis:Stephen A. Lauer, MS, PhD *; Kyra H. Grantz, BA *; Qifang Bi, MHS; Forrest K. Jones, MPH; Qulu Zheng, MHS; Hannah R. Meredith, PhD; Andrew S. Azman, PhD; Nicholas G. Reich, PhD; Justin Lessler, PhD. 2020. The Incubation Period of Coronavirus Disease 2019 (COVID-19) From Publicly Reported Confirmed Cases: Estimation and Application. Annals of Internal Medicine DOI: 10.7326/M20-0504.New Cases per Day (NCD) = Measures the daily spread of COVID-19. This is the basis for all rates. Back-casting revisions: In the Johns Hopkins’ data, the structure is to provide the cumulative number of cases per day, which presumes an ever-increasing sequence of numbers, e.g., 0,0,1,1,2,5,7,7,7, etc. However, revisions do occur and would look like, 0,0,1,1,2,5,7,7,6. To accommodate this, we revised the lists to eliminate decreases, which make this list look like, 0,0,1,1,2,5,6,6,6.Reporting Interval: In the early weeks, Johns Hopkins' data provided reporting every day regardless of change. In late April, this changed allowing for days to be skipped if no new data was available. The day was still included, but the value of total cases was set to Null. The processing therefore was updated to include tracking of the spacing between intervals with valid values.100 News Cases in a day as a spike threshold: Empirically, this is based on COVID-19’s rate of spread, or r0 of ~2.5, which indicates each case will infect between two and three other people. There is a point at which each administrative area’s capacity will not have the resources to trace and account for all contacts of each patient. Thus, this is an indicator of uncontrolled or epidemic trend. Spiking activity in combination with the rate of new cases is the basis for determining whether an area has a spreading or epidemic trend (see below). Source used as basis:World Health Organization (WHO). 16-24 Feb 2020. Report of the WHO-China Joint Mission on Coronavirus Disease 2019 (COVID-19). Obtained online.Mean of Recent Tail of NCD = Empirical, and a COVID-19-specific basis for establishing a recent trend. The recent mean of NCD is taken from the most recent fourteen days. A minimum of 21 days of cases is required for analysis but cannot be considered reliable. Thus, a preference of 42 days of cases ensures much higher reliability. This analysis is not explanatory and thus, merely represents a likely trend. The tail is analyzed for the following:Most recent 2 days: In terms of likelihood, this does not mean much, but can indicate a reason for hope and a basis to share positive change that is not yet a trend. There are two worthwhile indicators:Last 2 days count of new cases is less than any in either the past five or 14 days. Past 2 days has only one or fewer new cases – this is an extremely positive outcome if the rate of testing has continued at the same rate as the previous 5 days or 14 days. Most recent 5 days: In terms of likelihood, this is more meaningful, as it does represent at short-term trend. There are five worthwhile indicators:Past five days is greater than past 2 days and past 14 days indicates the potential of the past 2 days being an aberration. Past five days is greater than past 14 days and less than past 2 days indicates slight positive trend, but likely still within peak trend time frame.Past five days is less than the past 14 days. This means a downward trend. This would be an

The world's population first reached one billion people in 1803, and reach eight billion in 2023, and will peak at almost 11 billion by the end of the century. Although it took thousands of years to reach one billion people, it did so at the beginning of a phenomenon known as the demographic transition; from this point onwards, population growth has skyrocketed, and since the 1960s the population has increased by one billion people every 12 to 15 years. The demographic transition sees a sharp drop in mortality due to factors such as vaccination, sanitation, and improved food supply; the population boom that follows is due to increased survival rates among children and higher life expectancy among the general population; and fertility then drops in response to this population growth. Regional differences The demographic transition is a global phenomenon, but it has taken place at different times across the world. The industrialized countries of Europe and North America were the first to go through this process, followed by some states in the Western Pacific. Latin America's population then began growing at the turn of the 20th century, but the most significant period of global population growth occurred as Asia progressed in the late-1900s. As of the early 21st century, almost two thirds of the world's population live in Asia, although this is set to change significantly in the coming decades. Future growth The growth of Africa's population, particularly in Sub-Saharan Africa, will have the largest impact on global demographics in this century. From 2000 to 2100, it is expected that Africa's population will have increased by a factor of almost five. It overtook Europe in size in the late 1990s, and overtook the Americas a decade later. In contrast to Africa, Europe's population is now in decline, as birth rates are consistently below death rates in many countries, especially in the south and east, resulting in natural population decline. Similarly, the population of the Americas and Asia are expected to go into decline in the second half of this century, and only Oceania's population will still be growing alongside Africa. By 2100, the world's population will have over three billion more than today, with the vast majority of this concentrated in Africa. Demographers predict that climate change is exacerbating many of the challenges that currently hinder progress in Africa, such as political and food instability; if Africa's transition is prolonged, then it may result in further population growth that would place a strain on the region's resources, however, curbing this growth earlier would alleviate some of the pressure created by climate change.

Although the pervasiveness of intraspecific wing-size polymorphism and transitions to flightlessness have long captivated biologists, the demographic outcomes of shifts in dispersal ability are not yet well understood and have been seldom studied at early stages of diversification. Here, we use genomic data to infer the consequences of dispersal-related trait variation in the taxonomically controversial short-winged (Chorthippus corsicus corsicus) and long-winged (Chorthippus corsicus pascuorum) Corsican grasshoppers. Our analyses revealed lack of contemporary hybridization between sympatric long- and short-winged forms and phylogenomic reconstructions supported their taxonomic distinctiveness, rejecting the hypothesis of intraspecific wing polymorphism. Statistical evaluation of alternative models of speciation strongly supported a scenario of Pleistocene divergence (<1.5 Ma) with ancestral gene flow. According to neutral expectations from differences in dispersal capacity, historical effective migration rates from the long- to the short-winged taxon were three-fold higher than in the opposite direction. Although populations of the two taxa present a marked genetic structure and have experienced parallel demographic histories, our coalescent-based analyses suggest that reduced dispersal has fueled diversification in the short-winged C. c. corsicus. Collectively, our study illustrates how dispersal reduction can speed up geographical diversification and increase the opportunity for allopatric speciation in topographically complex landscapes.

Methods Genomic library preparation We used NucleoSpin Tissue (Macherey-Nagel, Düren, Germany) kits to extract and purify DNA from a hind leg of each individual. We processed genomic DNA into one genomic library using the double-digestion restriction-site associated DNA sequencing procedure (ddRAD-seq) described in Peterson et al. (2012). In brief, we digested DNA with the restriction enzymes MseI and EcoRI (New England Biolabs, Ipswich, MA, USA) and ligated Illumina adaptors including unique 7-bp barcodes to the digested fragments of each individual. We pooled ligation products and size-selected them between 475-580 bp with a Pippin Prep instrument (Sage Science, Beverly, MA, USA). We amplified the fragments by PCR with 12 cycles using the iProofTM High-Fidelity DNA Polymerase (BIO-RAD, Veenendaal, Netherlands) and sequenced the library in a single-read 150-bp lane on an Illumina HiSeq2500 platform at The Centre for Applied Genomics (Toronto, ON, Canada). Genomic data assembling and filtering Raw sequences were demultiplexed and preprocessed using stacks v. 1.35 (Catchen et al., 2013) and assembled using pyrad v. 3.0.66 (Eaton, 2014). Libraries were demultiplexed and filtered for overall quality using process_radtags (Catchen et al., 2011, 2013), retaining reads with a Phred score > 10 (using a sliding window of 15%), no adaptor contamination, and that had an unambiguous barcode and restriction cut site. Raw sequence data quality was checked in fastqc v. 0.11.5 (http://www.bioinformatics.babraham.ac.uk/projects/fastqc/) and sequences were trimmed to 129 bp using seqtk (Heng Li, https://github.com/lh3/seqtk) in order to remove barcodes and low-quality reads near the 3´ ends. We assembled our sequences into de novo loci using pyrad v. 3.0.66 (Eaton, 2014). Briefly, reads retained after process_radtags were further quality-filtered with pyrad to convert base calls with a Phred score <20 into Ns and discard reads with >2 Ns. Retained reads were clustered within- and across samples considering a threshold of sequence similarity (Wclust) of 85% and clusters with a coverage depth <5 were discarded. Loci containing one or more heterozygous sites across >15% of individuals were excluded, as we expect that this represents a fixed difference among clustered paralogs rather than a true polymorphism (Eaton, 2014). Unless otherwise indicated, all downstream analyses were performed using datasets of unlinked SNPs (i.e., a single SNP per RAD locus) obtained with pyrad considering a clustering threshold of sequence similarity of 0.85 (Wclust = 0.85) and discarding loci that were not present in at least 50 % individuals (minCov = 50 %). References Catchen, J. M., A. Amores, P. Hohenlohe, W. Cresko, and J. H. Postlethwait. 2011. stacks: Building and genotyping loci de novo from short-read sequences. G3-Genes Genom. Genet. 1:171-182. Catchen, J., P. A. Hohenlohe, S. Bassham, A. Amores, and W. A. Cresko. 2013. stacks: an analysis tool set for population genomics. Mol. Ecol. 22:3124-3140. Eaton, D. A. R. 2014. pyrad: assembly of de novo RADseq loci for phylogenetic analyses. Bioinformatics 30:1844-1849. Peterson, B. K., J. N. Weber, E. H. Kay, H. S. Fisher, and H. E. Hoekstra. 2012. Double digest RADseq: An inexpensive method for de novo SNP discovery and genotyping in model and non-model species. PLoS One 7:e37135.

Abstract

The transition from socialism to a market economy has transformed the lives of many people. What are people's perceptions and attitudes to transition? What are the current attitudes to market reforms and political institutions?

To analyze these issues, the EBRD and the World Bank have jointly conducted the comprehensive, region-wide "Life in Transition Survey" (LiTS), which combines traditional household survey features with questions about respondents' attitudes and is carried out through two-stage sampling with a random selection of households and respondents.

The LiTS assesses the impact of transition on people through their personal and professional experiences during the first 15 years of transition. LiTS attempts to understand how these personal experiences of transition relate to people’s attitudes toward market and political reforms, as well as their priorities for the future.

The main objective of the LiTS was to build on existing studies to provide a comprehensive assessment of relationships among life satisfaction and living standards, poverty and inequality, trust in state institutions, satisfaction with public services, attitudes to a market economy and democracy and to provide valuable insights into how transition has affected the lives of people across a region comprising 16 countries in Central and Eastern Europe (“CEE”) and 11 in the Commonwealth of Independent State (“CIS”). Turkey and Mongolia were also included in the survey.

Geographic coverage

The LITS was to be implemented in the following 29 countries: Albania, Armenia, Azerbaijan, Belarus, Bosnia and Herzegovina, Bulgaria, Croatia, Czech Republic, Estonia, Former Yugoslav Republic of Macedonia (FYROM), Georgia, Hungary, Kazakhstan, Kyrgyz Republic, Latvia, Lithuania, Moldova, Mongolia, Poland, Romania, Russia, Serbia and Montenegro, Slovak Republic, Slovenia, Tajikistan, Turkey, Turkmenistan, Ukraine and Uzbekistan.

Kind of data

Sample survey data [ssd]

Sampling procedure

A total of 1,000 face-to-face household interviews per country were to be conducted, with adult (18 years and over) occupants and with no upper limit for age. The sample was to be nationally representative. The EBRD’s preferred procedure was a two stage sampling method, with census enumeration areas (CEA) as primary sampling units and households as secondary sampling units. To the extent possible, the EBRD wished the sampling procedure to apply no more than 2 stages.

The first stage of selection was to use as a sampling frame the list of CEA's generated by the most recent census. Ideally, 50 primary sampling units (PSU's) were to be selected from that sample frame, with probability proportional to size (PPS), using as a measure of size either the population, or the number of households.

The second sampling stage was to select households within each of the primary sampling units, using as a sampling frame a specially developed list of all households in each of the selected PSU's defined above. Households to be interviewed were to be selected from that list by systematic, equal probability sampling. Twenty households were to be selected in each of the 50 PSU's.

The individuals to be interviewed in each household were to be selected at random, within each of the selected households, with no substitution if possible.

ESTABLISHMENT OF THE SAMPLE FRAME OF PSU’s

In each country we established the most recent sample frame of PSU’s which would best serve the purposes of the LITS sampling methodology. Details of the PSU sample frames in each country are shown in table 1 (page 10) of the survey report.

In the cases of Armenia, Azerbaijan, Kazakhstan, Serbia and Uzbekistan, CEA’s were used. In Croatia we also used CEA’s but in this case, because the CEA’s were very small and we would not have been able to complete the targeted number of interviews within each PSU, we merged together adjoining CEA’s and constructed a sample of 1,732 Merged Enumeration Areas. The same was the case in Montenegro.

In Estonia, Hungary, Lithuania, Poland and the Slovak Republic we used Eurostat’s NUTS area classification system.

[NOTE: The NUTS (from the French "Nomenclature des territoriales statistiques" or in English ("Nomenclature of territorial units for statistics"), is a uniform and consistent system that runs on five different NUTS levels and is widely used for EU surveys including the Eurobarometer (a comparable survey to the Life in Transition). As a hierarchical system, NUTS subdivides the territory of the country into a defined number of regions on NUTS 1 level (population 3-7 million), NUTS 2 level (800,000-3 million) and NUTS 3 level (150,000-800,000). At a more detailed level NUTS 3 is subdivided into smaller units (districts and municipalities). These are called "Local Administrative Units" (LAU). The LAU is further divided into upper LAU (LAU1 - formerly NUTS 4) and LAU 2 (formerly NUTS 5).]

Albania, Bulgaria, the Czech Republic, Georgia, Moldova and Romania used the electoral register as the basis for the PSU sample frame. In the other cases, the PSU sample frame was chosen using either local geographical or administrative and territorial classification systems. The total number of PSU sample frames per country varied from 182 in the case of Mongolia to over 48,000 in the case of Turkey. To ensure the safety of our fieldworkers, we excluded from the sample frame PSU’s territories (in countries such as Georgia, Azerbaijan, Moldova, Russia, etc) in which there was conflict and political instability. We have also excluded areas which were not easily accessible due to their terrain or were sparsely populated.

In the majority of cases, the source for this information was the national statistical body for the country in question, or the relevant central electoral committee. In establishing the sample frames and to the extent possible, we tried to maintain a uniform measure of size namely, the population aged 18 years and over which was of more pertinence to the LITS methodology. Where the PSU was based on CEA’s, the measure was usually the total population, whereas the electoral register provided data on the population aged 18 years old and above, the normal voting age in all sampled countries. Although the NUTS classification provided data on the total population, we filtered, where possible, the information and used as a measure of size the population aged 18 and above. The other classification systems used usually measure the total population of a country. However, in the case of Azerbaijan, which used CEA’s, and Slovenia, where a classification system based on administrative and territorial areas was employed, the measure of size was the number of households in each PSU.

The accuracy of the PSU information was dependent, to a large extent, on how recently the data has been collected. Where the data were collected recently then the information could be considered as relatively accurate. However, in some countries we believed that more recent information was available, but because the relevant authorities were not prepared to share this with us citing secrecy reasons, we had no alternative than to use less up to date data. In some countries the age of the data available makes the figures less certain. An obvious case in point is Bosnia and Herzegovina, where the latest available figures date back to 1991, before the Balkan wars. The population figures available take no account of the casualties suffered among the civilian population, resulting displacement and subsequent migration of people.

Equally there have been cases where countries have experienced economic migration in recent years, as in the case of those countries that acceded to the European Union in May, 2004, such as Hungary, Poland and the Baltic states, or to other countries within the region e.g. Armenians to Russia, Albanians to Greece and Italy; the available figures may not accurately reflect this. And, as most economic migrants tend to be men, the actual proportion of females in a population was, in many cases, higher than the available statistics would suggest. People migration in recent years has also occurred from rural to urban areas in Albania and the majority of the Asian Republics, as well as in Mongolia on a continuous basis but in this case, because of the nomadic population of the country.

SAMPLING METHODOLOGY

Brief Overview

In broad terms the following sampling methodology was employed: · From the sample frame of PSU’s we selected 50 units · Within each selected PSU, we sampled 20 households, resulting in 1,000 interviews per country · Within each household we sampled 1 and sometimes 2 respondents The sampling procedures were designed to leave no free choice to the interviewers. Details on each of the above steps as well as country specific procedures adapted to suit the availability, depth and quality of the PSU information and local operational issues are described in the following sections.

Selection of PSU’s

The PSU’s of each country (all in electronic format) were sorted first into metropolitan, urban and rural areas (in that order), and within each of these categories by region/oblast/province in alphabetical order. This ensured a consistent sorting methodology across all countries and also that the randomness of the selection process could be supervised.

To select the 50 PSU’s from the sample frame of PSU’s, we employed implicit stratification and sampling was done with PPS. Implicit stratification ensured that the sample of PSU’s was spread across the primary categories of explicit variables and a better representation of the population, without actually stratifying the PSU’s thus, avoiding difficulties in calculating the sampling errors at a later stage. In brief, the PPS involved the

Abstract

The DHS is intended to serve as a primary source for international population and health information for policymakers and for the research community. In general, DHS has four objectives: - To provide participating countries with a database and analysis useful for informed choices, - To expand the international population and health database, - To advance survey methodology, and - To help develop in participating countries technical skills and resources necessary to conduct demographic and health surveys.

Apart from estimating fertility and contraceptive prevalence rates, DHS also covers the topic of child health, which has become the focus of many development programs aimed at improving the quality of life in general. The Indonesian DHS survey did not include health-related questions because this information was collected in the 1987 SUSENAS in more detail and with wider geographic coverage. Hence, the Indonesian DHS was named the "National Indonesian Contraceptive Prevalence Survey" (NICPS).

The National Indonesia Contraceptive Prevalence Survey (NICPS) was a collaborative effort between the Indonesian National Family Planning Coordinating Board (NFPCB), the Institute for Resource Development of Westinghouse and the Central Bureau of Statistics (CBS). The survey was part of an international program in which similar surveys are being implemented in developing countries in Asia, Africa, and Latin America.

The 1987 NICPS was specifically designed to meet the following objectives: - To provide data on the family planning and fertility behavior of the Indonesian population necessary for program organizers and policymakers in evaluating and enhancing the national family planning program, and - To measure changes in fertility and contraceptive prevalence rates and at the same time study factors which affect the change, such as marriage patterns, urban/rural residence, education, breastfeeding habits, and availability of contraception.

Geographic coverage

National

Analysis unit

• Household
• Children under five years
• Women age 15-49
• Men

Kind of data

Sample survey data

Sampling procedure

The 1987 NICPS sample was drawn from the annual National Socioeconomic Survey (popularly called SUSENAS) which was conducted in January and February 1987. Each year the SUSENAS consists of one set of core questions and several modules which are rotated every three years. The 1987 SUSENAS main modules covered household income, expenditure, and consumption. In addition, in collaboration with the Ministry of Health, information pertaining to children under 5 years of age was collected, including food supplement patterns, and measurement of height, weight, and arm circumference. In this module, information on prenatal care, type of birth attendant, and immunization was also asked.

This national survey covered over 60,000 households which were scattered in almost all of the districts. The data were collected by the "Mantri Statistik", a CBS officer in charge of data collection at the sub-district level. All households covered in the selected census blocks were listed on the SSN 87-LI form. This form was then used in selecting samples for each of the modules included in the SUSENAS. This particular form was also used to select the sample households in the 1987 NICPS.

Sample selection in the 1987 SUSENAS utilized a multistage sampling procedure. The first stage consisted of selecting a number of census blocks with probability proportional to the number of households in the block. Census blocks are statistical areas formed before the 1980 Population Census and contain approximately 100 households. At the second stage, households were selected systematically from each sampled census block.

Selection of the 1987 NICPS sample was also done in two stages. The first stage was to select census blocks from the those selected in the 1987 SUSENAS. At the second stage a number of households was selected systematically from the selected census block.

Mode of data collection

Face-to-face [f2f]

Research instrument

The household questionnaire was used to record all members of the selected households who usually live in the household. The questionnaire was utilized to identify the eligible respondents in the household, and to provide the numerator for the computation of demographic measurements such as fertility and contraceptive use rates.

The individual questionnaire was used for all ever-married women aged 15-49, and consisted of the following eight sections:

Section 1 Respondent's Background

This part collected information related to the respondent and the household, such as current and past mobility, age, education, literacy, religion, and media exposure. Information related to the household includes source of water for drinking, for bathing and washing, type of toilet, ownership of durable goods, and type of floor.

Section 2 Reproduction

This part gathered information on all children ever born, sex of the child, month and year of birth, survival status of the child, age when the child died, and whether the child lived with the respondent. Using the information collected in this section, one can compute measures of fertility and mortality, especially infant and child mortality rates. With the birth history data collected in this section, it is possible to calculate trends in fertility over time. This section also included a question about whether the respondent was pregnant at the time of interview, and her knowledge regarding women's fertile period in the monthly menstrual cycle.

Section 3 Knowledge and Practice of Family Planning

This section is one of the most important parts of the 1987 NICPS survey. Here the respondent was asked whether she had ever heard of or used any of the family planning methods listed. If the respondent had used a contraceptive method, she was asked detailed questions about the method. For women who gave birth to a child since January 1982, questions on family planning methods used in the intervals between births were also asked. The section also included questions on source of methods, quality of use, reasons for nonuse, and intentions for future use. These data are expected to answer questions on the effectiveness of family planning use. Finally, the section also included questions about whether the respondent had been visited by a family planning field worker, which community-level people she felt were most appropriate to give family planning information, and whether she had ever heard of the condom, DuaLima, the brand being promoted by a social marketing program.

Section 4 Breastfeeding

The objective of this part was to collect information on maternal and child health, primarily that concerning place of birth, type of assistance at birth, breastfeeding practices, and supplementary food. Information was collected for children born since January 1982.

Section 5 Marriage

This section gathered information regarding the respondent's age at first marriage, number of times married, and whether the respondent and her husband ever lived with any of their parents. Several questions in this section were related to the frequency of sexual intercourse to determine the respondent's risk of pregnancy. Not all of the data collected in this section are presented in this report; some require more extensive analysis than is feasible at this stage.

Section 6 Fertility Preferences

Intentions about having another child, preferred birth interval, and ideal number of children were covered in this section.

Section 7 Husband's Background and Respondent's Work

Education, literacy and occupation of the respondent's husband made up this section of the questionnaire. It also collected information on the respondent's work pattern before and after marriage, and whether she was working at the time of interview.

Section 8 Interview Particulars

This section was used to record the language used in the interview and information about whether the interviewer was assisted by an interpreter. The individual questionnaire also included information regarding the duration of interview and presence of other persons at particular points during the interview. In addition to the questionnaires, two manuals were developed. The manual for interviewers contained explanations of how to conduct an interview, how to carry out the field activity, and how to fill out the questionnaires. Since information regarding age was vital in this survey, a table to convert months from Javanese, Sundanese and Islamic calendar systems to the Gregorian calendar was attached to the 1987 NICPS manual for the interviewers.

Response rate

The NICPS covered a sample of nearly 15,000 households to interview 11,884 respondents. Respondents for the individual interview were ever-married women aged 15-49. During the data collection, 14,141 out of the 14,227 existing households and 11,884 out of 12,065 eligible women were successfully interviewed. In general, few problems were encountered during interviewing, and the response rate was high--99 percent for households and 99 percent for individual respondents.

Note: See APPENDIX A in the report for more information.

Sampling error estimates

The results from sample surveys are affected by two types of errors: (1) non-sampling error and (2) sampling error. Non-sampling error is due to mistakes made in carrying out field activities, such as failure to locate and interview the correct household, errors in the way questions are asked, misunderstanding of the questions on the part of either the interviewer or the respondent, data entry errors, etc. Although efforts were made during the design and

Abstract

The Genomic and environmental risk factors for cardiometabolic disease in Africans (AWI-Gen) project was a collaborative study between the University of the Witwatersrand (Wits) and the INDEPTH Network funded under the Human Heredity and Health in Africa (H3Africa) initiative. The H3Africa was a ground-breaking initiative to build institutional and individual capacity to undertake genetic and genomic studies in the African region. This collaboration, involved five INDEPTH sites i.e. 1) Navrongo - Ghana; 2) Nanoro - Burkina Faso; 3&4) Agincourt and Digkale - South Africa; and 5) Nairobi - Kenya) plus the Soweto-based birth-to-twenty cohort. AWI-Gen phase I was a population based cross-sectional study with a research platform of over 12,045 participants aged 40-60 years from Burkina Faso, Ghana, Kenya and South Africa. It aimed to understand the interplay between genetic, epigenetic and environmental risk factors for obesity and related cardiometabolic diseases (CMD) in sub-Saharan Africa and it generated epi-demographic, environmental, health history, behavioral, anthropometric, physiological and genetic data across a range of rapidly transitioning African settings. This provided a unique resource to examine genetic associations and gene-environment interactions that will contribute to Afrocentric risk prediction models and African-appropriate Mendelian Randomization instruments, and exploit their potential to improve personal and population health - while strengthening regional research capacity. We plan to continue this work in AWIGEN-phase II among the same participants recruited in AWIGen-I offering an opportunity to examine data in a longitudinal manner. The AWI-Gen phase II project aims to establish the genomic contribution to CMD and risk at a time when multiple interacting transitions, in the presence of high background HIV or malaria prevalence, are driving a rapid escalation in CMD across the African continent. The project capitalizes on the unique strengths of existing longitudinal cohorts and well-established health and demographic surveillance systems(HDSS) run by the partner institutions. The six study sites represent geographic and social variability of African populations which are also at different stages of the demographic and epidemiological transitions. The work in Kenya will be undertaken in the Nairobi Urban Health and Demographic Surveillance System (NUHDSS) run by African Population and Health Research Center (APHRC) following participants who were recruited in AWIGEN-Phase I. AWI-Gen II consisted of five main aims: i) AIM-1 (Sub-study 1): Genetic associations studies to elucidate functional pathways involved in determining body composition and risk for CMD by detecting pivotal genomic and environmental contributors; ii) AIM 2 (Sub-study 2): Genomics and bioinformatics-impact of genomic diversity on disease risk and precision public health; iii) AIM 3 (Sub-study 3): Examine changes over the menopausal transition in body composition and CMD risk; iv) AIM 4 (Sub-study 4): Examine gut microbiome in older adults and its relationship to obesity, diabetes and glucose tolerance and ageing; and v) AIM 5 (Sub-study 5): Explore respiratory disease in context of multi-morbidity. In this application, we sought ethical approval for the Kenya study only. The other partners sought approval from their appropriate ethics review authorities in their countries. The study budget was $248,613 and was funded by National Institute of Health (NIH)-USA under H3Africa. Data collection was undertaken for approximately 12 months but sample processing, data analysis, manuscript writing, capacity building and policy engagement was continued up to three years after field work (up to 2022).

Geographic coverage

County coverage (Informal settlements of Korogocho and Viwandani in Nairobi)

Analysis unit

Individual Household

Universe

The survey covered individual participants aged 45-65 years.

Sampling procedure

a) Study design: A prospective cohort study to examine genetic associations and gene-environment interactions with measures of change in CMD and risk derived over 5 years (AWI-Gen I survey was in 2014/2015, and survey for phenotypic characteristics (under AWI-Gen II) among the same individuals will was repeated in 2019/2020). This was an extend baseline (AWI-Gen I) to provide longitudinal data (AWI-Gen II). b) Study site (geographical) The study in Kenya was conducted in Nairobi, specifically in Korogocho and Viwandani urban informal settlements which are covered by the NUHDSS. c) Study populations Sub-study 1 & 5: Adult (40-60 years at baseline) residents of Korogocho and Viwandani informal settlements registered in the NUHDSS. Sample size A sample size of 2000 per site (12000 in total) was used in AWIGEN-I based on power calculations and effect sizes. The power calculations show that we have power to detect realistic effect sizes, based on studies in other populations. Figure 2 illustrates the relationship between power and effect size for two different phenotypes, illustrating that the detectable effect size is realistic. Power analysis for a sample size of 12000 individuals based on proposed candidate gene study for BMI (shown on the left) and for DXA (total body fat) (shown on the right). Given a sample size of 12000 in the AWI-Gen study, this graph shows effect size (x) which could be detected at a given power (y) for different minor allele frequencies (ranging from 0.05-045). For example, with a minor allele frequency of 0.25, we will have 80% power to detect an effect size (Beta) of 0.20 per allele change in BMI, and an effect size of 0.25 per allele change in body fat percentage. For AWIGEN 2, we will follow the same participants. We anticipate a retention of 70% from the 2000 participants recruited in phase 1. Thus, our sample size for AWIGEN-11 was approximately 1400 participants for the Kenyan site to for sub-studies 1 and 2. For Sub-studies 3 & 4 we will randomly sample 250 individuals for each sub-study which is a large sample by most microbiome project standards. For Sub-study 5 we will include all participants selected in Sub-study 1

Sampling deviation

N/A

Mode of data collection

Other [oth]

Research instrument

The questionnaire for AWIGen 2 was a structured questionnaire developed by the University of Witwatersrand. The questionnaire was translated from English to Swahili. The individual questionnaire was administered to an adult (40-60 years old), which collected various information of the individual including, age, gender, BMI, Visceral fat levels, T2 diabetes status, blood pressure, socio-economic status, lifestyle (diet, tobacco, alcohol, exercise etc.) and HIV infection status. In addition, for participants in microbiome study we will ask information on antibiotics use. We will repeat the anthropometric measurements including height, weight, waist and hip circumference and ultrasound measurements of visceral and subcutaneous fat, and cIMT.

Cleaning operations

Data was edited on REDCap during data entry and also secondary editing was performed once the files were submitted to the server.

Response rate

59%

Sampling error estimates

N/A

There are approximately 8.16 billion people living in the world today, a figure that shows a dramatic increase since the beginning of the Common Era. Since the 1970s, the global population has also more than doubled in size. It is estimated that the world's population will reach and surpass 10 billion people by 2060 and plateau at around 10.3 billion in the 2080s, before it then begins to fall. Asia When it comes to number of inhabitants per continent, Asia is the most populous continent in the world by a significant margin, with roughly 60 percent of the world's population living there. Similar to other global regions, a quarter of inhabitants in Asia are under 15 years of age. The most populous nations in the world are India and China respectively; each inhabit more than three times the amount of people than the third-ranked United States. 10 of the 20 most populous countries in the world are found in Asia. Africa Interestingly, the top 20 countries with highest population growth rate are mainly countries in Africa. This is due to the present stage of Sub-Saharan Africa's demographic transition, where mortality rates are falling significantly, although fertility rates are yet to drop and match this. As much of Asia is nearing the end of its demographic transition, population growth is predicted to be much slower in this century than in the previous; in contrast, Africa's population is expected to reach almost four billion by the year 2100. Unlike demographic transitions in other continents, Africa's population development is being influenced by climate change on a scale unseen by most other global regions. Rising temperatures are exacerbating challenges such as poor sanitation, lack of infrastructure, and political instability, which have historically hindered societal progress. It remains to be seen how Africa and the world at large adapts to this crisis as it continues to cause drought, desertification, natural disasters, and climate migration across the region.

The study curates demographic information of a short-lived native iterocarpic herb, Cirsium undulatum. This data was collected from 1990 to 2009 at two study sites, Arapaho Prairie Preserves (APP) and Niobrara Valley Preserve (NVP), in the Nebraska Sandhills, USA. At APP, four 12m by 12m plots were established. At NVP, three 12m by 12m plots were established. The data associated with each plot are as described below:1. "UAASOverview19902009RAMET 20230630.xlsx" = Original Transition data for Plot A in APP2. "UABSOverview19902009RAMET 20230630.xlsx" = Original Transition data for Plot B in APP3. "UACSOverview19902009RAMET 20230630.xlsx" = Original Transition data for Plot C in APP4. "UADSOverview19902009RAMET 20230630.xlsx" = Original Transition data for Plot D in APP5. "UNMSOverview19902009RAMET 20230630.xlsx" = Original Transition data for Plot M in NVP6. "UNKSOverview19902009RAMET 20230630.xlsx" = Original Transition data for Plot K in NVP7. "UNLSOverview19902009RAMET 20230630.xlsx" = Original Transition data for Plot L in NVPIn 1990 – 1999, ramets in the plots were monitored twice a year: in early season (May) and in late season (late June - mid July); plots at both sites were examined within one week each year. In 4 years (2000 - 2004), all ramets were measured in late May, but in general, only flowering ramets were remeasured in July; however, some late-season observations were recorded on non-flowering ramets. In 5 years late in the study (2005 – 2009), the ramets were measured only once, in mid- to late-season: in mid-July for 3 years (2005 - 2007) and in late June for two years (2008 - 2009). New ramets observed in 2008 – 2009 at the end of the study were no longer given numerical tags; so, those did not contribute to the estimation of specific stage transition rates.This data is associated with a current ongoing project which examines the time-lag effect of weather variables on the vital rates of Cirsium undulatum. The data was collected by Dr. Svata Louda (a retired Professor from the School of Biological Sciences, University of Nebraska, Lincoln) with the help of her collaborators: A. Arnett, T. A. Rand, F. L. Russell, R. W. Otley, 18 graduate students, and 21 undergraduate students over the 20 years. The data provided here was extracted by Dr. Kathleen Keeler (also a retired Professor from the School of Biological Sciences, University of Nebraska, Lincoln) from the field data records.We want to acknowledge that this study was done on the homelands of the Lakota People (Arapaho Prairie Preserve) and of the Ponca People (Niobrara Valley Preserve); we honor this legacy. We are also indebted to The Nature Conservancy, Nebraska Chapter, for permission to work in these reserves and to their incredible staff for all of the support and encouragement provided for the study, especially A. A. Steuter at the beginning of the study.

Polar regions are experiencing some of the most dramatic effects of climate change resulting in large-scale changes in sea ice cover. Despite this, there are relatively few long-term studies on polar species that evaluate the full scope of these effects. Over the last two decades, this team has conducted globally unique demographic studies of Adélie penguins in the Ross Sea, Antarctica, to explore several potential mechanisms for population change. This five-year project will use penguin-borne sensors to evaluate foraging conditions and behavior and environmental conditions on early life stages of Adélie penguins. Results will help to better understand population dynamics and how populations might respond to future environmental change. To promote STEM literacy, education and public outreach efforts will include multiple activities. The PenguinCam and PenguinScience.com website (impacts of >1 million hits per month and use by >300 classrooms/~10,000 students) will be continued. Each field season will also have ‘Live From the Penguins’ Skype calls to classes (~120/season). Classroom-ready activities that are aligned with Next Generation Science Standards will be developed with media products and science journal papers translated to grade 5-8 literacy level. The project will also train early career scientists, postdoctoral scholars, graduate students and post-graduate interns. Finally, in partnership with an Environmental Leadership Program, the team will host 2-year Roger Arliner Young Conservation Fellow, which is a program designed to increase opportunities for recent college graduates of color to learn about, engage with, and enter the environmental conservation sector.