This dataset includes data on 25 transitions of a matrix demographic model of the invasive species Vincetoxicum nigrum (L.) Moench (black swallow-wort or black dog-strangling vine) and Vincetoxicum rossicum (Kleopow) Barb. (pale swallow-wort or dog-strangling vine) (Apocynaceae, subfamily Asclepiadoideae), two invasive perennial vines in the northeastern U.S.A. and southeastern Canada. The matrix model was developed for projecting population growth rates as a result of changes to lower-level vital rates from biological control although the model is generalizable to any control tactic. Transitions occurred among the five life stages of seeds, seedlings, vegetative juveniles (defined as being in at least their second season of growth), small flowering plants (having 1–2 stems), and large flowering plants (having 3 or more stems). Transition values were calculated using deterministic equations and data from 20 lower-level vital rates collected from 2009-2012 from two open field and two forest understory populations of V. rossicum (43°51’N, 76°17’W; 42°48'N, 76°40'W) and two open field populations of V. nigrum (41°46’N, 73°44’W; 41°18’N, 73°58’W) in New York State. Sites varied in plant densities, soil depth, and light levels (forest populations). Detailed descriptions of vital rate data collection may be found in: Milbrath et al. 2017. Northeastern Naturalist 24(1):37-53. Five replicate sets of transition data obtained from five separate spatial regions of a particular infestation were produced for each of the six populations. Note: Added new excel file of vital rate data on 12/7/2018. Resources in this dataset:Resource Title: Matrix model transition data for Vincetoxicum species. File Name: Matrix_model_transition_data.csvResource Description: This data set includes data on 25 transitions of a matrix demographic model of two invasive Vincetoxicum species from six field and forest populations in New York State.Resource Title: Variable definitions. File Name: Matrix_model_metadata.csvResource Description: Definitions of variables including equations for each transition and definitions of the lower-level vital rates in the equationsResource Title: Vital Rate definitions. File Name: Vital_Rate.csvResource Description: Vital Rate definitions of lower-level vital rates used in transition equations - to be substituted into the Data Dictionary for full definition of each transition equation.Resource Title: Data Dictionary. File Name: Matrix_Model_transition_data_DD.csvResource Description: See Vital Rate resource for definitions of lower-level vital rates used in transition equations where noted.Resource Title: Matrix model vital rate data for Vincetoxicum species. File Name: Matrix_model_vital rate_data.csvResource Description: This data set includes data on 20 lower-level vital rates used in the calculation of transitions of a matrix demographic model of two invasive Vincetoxicum species in New York State as well as definitions of the vital rates. (File added on 12/7/2018)Resource Software Recommended: Microsoft Excel,url: https://office.microsoft.com/excel/

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

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.

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.

The total fertility rate of the world has dropped from around five children per woman in 1950, to 2.2 children per woman in 2025, which means that women today are having fewer than half the number of children that women did 75 years ago. Replacement level fertility This change has come as a result of the global demographic transition, and is influenced by factors such as the significant reduction in infant and child mortality, reduced number of child marriages, increased educational and vocational opportunities for women, and the increased efficacy and availability of contraception. While this change has become synonymous with societal progress, it does have wide-reaching demographic impact - if the global average falls below replacement level (roughly 2.1 children per woman), as is expected to happen in the 2050s, then this will lead to long-term population decline on a global scale. Regional variations When broken down by continent, Africa is the only region with a fertility rate above the global average, and, alongside Oceania, it is the only region with a fertility rate above replacement level. Until the 1980s, the average woman in Africa could expect to have 6-7 children over the course of their lifetime, and there are still several countries in Africa where women can still expect to have five or more children in 2025. Historically, Europe has had the lowest fertility rates in the world over the past century, falling below replacement level in 1975. Europe's population has grown through a combination of migration and increasing life expectancy, however even high immigration rates could not prevent its population from going into decline in 2021.

In 2023, it is estimated that the BRICS countries have a combined population of 3.25 billion people, which is over 40 percent of the world population. The majority of these people live in either China or India, which have a population of more than 1.4 billion people each, while the other three countries have a combined population of just under 420 million. Comparisons Although the BRICS countries are considered the five foremost emerging economies, they are all at various stages of the demographic transition and have different levels of population development. For all of modern history, China has had the world's largest population, but rapidly dropping fertility and birth rates in recent decades mean that its population growth has slowed. In contrast, India's population growth remains much higher, and it is expected to overtake China in the next few years to become the world's most populous country. The fastest growing population in the BRICS bloc, however, is that of South Africa, which is at the earliest stage of demographic development. Russia, is the only BRICS country whose population is currently in decline, and it has been experiencing a consistent natural decline for most of the past three decades. Growing populations = growing opportunities Between 2000 and 2026, the populations of the BRICS countries is expected to grow by 625 million people, and the majority of this will be in India and China. As the economies of these two countries grow, so too do living standards and disposable income; this has resulted in the world's two most populous countries emerging as two of the most profitable markets in the world. China, sometimes called the "world's factory" has seen a rapid growth in its middle class, increased potential of its low-tier market, and its manufacturing sector is now transitioning to the production of more technologically advanced and high-end goods to meet its domestic demand.

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.

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.

Population dynamics, its types. Population migration (external, internal), factors determining it, main trends. Impact of migration on population health.

Under the guidance of Moldoev M.I. Sir By Riya Patil and Rutuja Sonar

Abstract

Population dynamics influence development and vice versa, at various scale levels: global, continental/world-regional, national, regional, and local. Debates on how population growth affects development and how development affects population growth have already been subject of intensive debate and controversy since the late 18th century, and this debate is still ongoing. While these two debates initially focused mainly on natural population growth, the impact of migration on both population dynamics and development is also increasingly recognized. While world population will continue growing throughout the 21st century, there are substantial and growing contrasts between and within world-regions in the pace and nature of that growth, including some countries where population is stagnating or even shrinking. Because of these growing contrasts, population dynamics and their interrelationships with development have quite different governance implications in different parts of the world.

1. Population Dynamics

Population dynamics refers to the changes in population size, structure, and distribution over time. These changes are influenced by four main processes:

Birth rate (natality)

Death rate (mortality)

Immigration (inflow of people)

Emigration (outflow of people)

Types of Population Dynamics

Natural population change: Based on birth and death rates.

Migration-based change: Caused by people moving in or out of a region.

Demographic transition: A model that explains changes in population growth as societies industrialize.

Population distribution: Changes in where people live (urban vs rural).

2. Population Migration

Migration refers to the movement of people from one location to another, often across political or geographical boundaries.

Types of Migration

External migration (international):

Movement between countries.

Examples: Refugee relocation, labor migration, education.

Internal migration:

Movement within the same country or region.

Examples: Rural-to-urban migration, inter-state migration.

3. Factors Determining Migration

Migration is influenced by push and pull factors:

Push factors (reasons to leave a place):

Unemployment

Conflict or war

Natural disasters

Poverty

Lack of services or opportunities

Pull factors (reasons to move to a place):

Better job prospects

Safety and security

Higher standard of living

Education and healthcare access

Family reunification

4. Main Trends in Migration

Urbanization: Mass movement to cities for work and better services.

Global labor migration: Movement from developing to developed countries.

Refugee and asylum seeker flows: Due to conflict or persecution.

Circular migration: Repeated movement between two or more locations.

Brain drain/gain: Movement of skilled labor away from (or toward) a country.

5. Impact of Migration on Population Health

Positive Impacts:

Access to better healthcare (for migrants moving to better systems).

Skills and knowledge exchange among health professionals.

Remittances improving healthcare affordability in home countries.

Negative Impacts:

Migrants’ health risks: Increased exposure to stress, poor living conditions, and occupational hazards.

Spread of infectious diseases: Especially when health screening is lacking.

Strain on health services: In receiving areas, especially with sudden or large influxes.

Mental health challenges: Due to cultural dislocation, discrimination, or trauma.

Population dynamics is one of the fundamental areas of ecology, forming both the basis for the study of more complex communities and of many applied questions. Understanding population dynamics is the key to understanding the relative importance of competition for resources and predation in structuring ecological communities, which is a central question in ecology.

Population dynamics plays a central role in many approaches to preserving biodiversity, which until now have been primarily focused on a single species approach. The calculation of the intrinsic growth rate of a species from a life table is often the central piece of conservation plans. Similarly, management of natural resources, such as fisheries, depends on population dynamics as a way to determine appropriate management actions.

Population dynamics can be characterized by a nonlinear system of difference or differential equations between the birth sizes of consecutive periods. In such a nonlinear system, when the feedback elasticity of previous events on current birth size is larger, the more likely the dynamics will be volatile. Depending on the classification criteria of the population, the revealed cyclical behavior has various interpretations. Under different contextual scenarios, Malthusian cycles, Easterlin cycles, predator–prey cycles, dynastic cycles, and capitalist–laborer cycles have been introduced and analyzed

Generally, population dynamics is a nonlinear stochastic process. Nonlinearities tend to be complicated to deal with, both when we want to do analytic stochastic modelling and when analysing data. The way around the problem is to approximate the nonlinear model with a linear one, for which the mathematical and statistical theories are more developed and tractable. Let us assume that the population process is described as:

(1)Nt=f(Nt−1,εt)

where Nt is population density at time t and εt is a series of random variables with identical distributions (mean and variance). Function f specifies how the population density one time step back, plus the stochastic environment εt, is mapped into the current time step. Let us assume that the (deterministic) stationary (equilibrium) value of the population is N* and that ε has mean ε*. The linear approximation of Eq. (1) close to N* is then:

(2)xt=axt−1+bϕt

where xt=Nt−N*, a=f

f(N*,ε*)/f

N, b=ff(N*,ε*)/fε, and ϕt=εt−ε*

The term population refers to the members of a single species that can interact with each other. Thus, the fish in a lake, or the moose on an island, are clear examples of a population. In other cases, such as trees in a forest, it may not be nearly so clear what a population is, but the concept of population is still very useful.

Population dynamics is essentially the study of the changes in the numbers through time of a single species. This is clearly a case where a quantitative description is essential, since the numbers of individuals in the population will be counted. One could begin by looking at a series of measurements of the numbers of particular species through time. However, it would still be necessary to decide which changes in numbers through time are significant, and how to determine what causes the changes in numbers. Thus, it is more sensible to begin with models that relate changes in population numbers through time to underlying assumptions. The models will provide indications of what features of changes in numbers are important and what measurements are critical to make, and they will help determine what the cause of changes in population levels might be.

To understand the dynamics of biological populations, the study starts with the simplest possibility and determines what the dynamics of the population would be in that case. Then, deviations in observed populations from the predictions of that simplest case would provide information about the kinds of forces shaping the dynamics of populations. Therefore, in describing the dynamics in this simplest case it is essential to be explicit and clear about the assumptions made. It would not be argued that the idealized population described here would ever be found, but that focusing on the idealized population would provide insight into real populations, just as the study of Newtonian mechanics provides understanding of more realistic situations in physics.

Population migration

The vast majority of people continue to live in the countries where they were born —only one in 30 are migrants.

In most discussions on migration, the starting point is usually numbers. Understanding changes in scale, emerging trends, and shifting demographics related to global social and economic transformations, such as migration, help us make sense of the changing world we live in and plan for the future. The current global estimate is that there were around 281 million international migrants in the world in 2020, which equates to 3.6 percent of the global population.

Overall, the estimated number of international migrants has increased over the past five decades. The total estimated 281 million people living in a country other than their countries of birth in 2020 was 128 million more than in 1990 and over three times the estimated number in 1970.

There is currently a larger number of male than female international migrants worldwide and the growing gender gap has increased over the past 20 years. In 2000, the male to female split was 50.6 to 49.4 per cent (or 88 million male migrants and 86 million female migrants). In 2020 the split was 51.9 to 48.1 per cent, with 146 million male migrants and 135 million female migrants. The share of

These data were developed by the Research & Analytics Department at the Atlanta Regional Commission using data from the U.S. Census Bureau across all standard and custom geographies at statewide summary level where applicable.For a deep dive into the data model including every specific metric, see the ACS 2019-2023. The manifest details ARC-defined naming conventions, field names/descriptions and topics, summary levels; source tables; notes and so forth for all metrics. Find naming convention prefixes/suffixes, geography definitions and user notes below.Prefixes:NoneCountpPercentrRatemMedianaMean (average)tAggregate (total)chChange in absolute terms (value in t2 - value in t1)pchPercent change ((value in t2 - value in t1) / value in t1)chpChange in percent (percent in t2 - percent in t1)sSignificance flag for change: 1 = statistically significant with a 90% CI, 0 = not statistically significant, blank = cannot be computedSuffixes:_e23Estimate from 2019-23 ACS_m23Margin of Error from 2019-23 ACS_e102006-10 ACS, re-estimated to 2020 geography_m10Margin of Error from 2006-10 ACS, re-estimated to 2020 geography_e10_23Change, 2010-23 (holding constant at 2020 geography)GeographiesAAA = Area Agency on Aging (12 geographic units formed from counties providing statewide coverage)ARC21 = Atlanta Regional Commission modeling area (21 counties merged to a single geographic unit)ARWDB7 = Atlanta Regional Workforce Development Board (7 counties merged to a single geographic unit)BeltLineStatistical (buffer)BeltLineStatisticalSub (subareas)Census Tract (statewide)CFGA23 = Community Foundation for Greater Atlanta (23 counties merged to a single geographic unit)City (statewide)City of Atlanta Council Districts (City of Atlanta)City of Atlanta Neighborhood Planning Unit (City of Atlanta)City of Atlanta Neighborhood Statistical Areas (City of Atlanta)County (statewide)CCDIST = County Commission Districts (statewide where applicable)CCSUPERDIST = County Commission Superdistricts (DeKalb)Georgia House (statewide)Georgia Senate (statewide)HSSA = High School Statistical Area (11 county region)MetroWater15 = Atlanta Metropolitan Water District (15 counties merged to a single geographic unit)Regional Commissions (statewide)State of Georgia (single geographic unit)Superdistrict (ARC region)US Congress (statewide)UWGA13 = United Way of Greater Atlanta (13 counties merged to a single geographic unit)ZIP Code Tabulation Areas (statewide)The user should note that American Community Survey data represent estimates derived from a surveyed sample of the population, which creates some level of uncertainty, as opposed to an exact measure of the entire population (the full census count is only conducted once every 10 years and does not cover as many detailed characteristics of the population). Therefore, any measure reported by ACS should not be taken as an exact number – this is why a corresponding margin of error (MOE) is also given for ACS measures. The size of the MOE relative to its corresponding estimate value provides an indication of confidence in the accuracy of each estimate. Each MOE is expressed in the same units as its corresponding measure; for example, if the estimate value is expressed as a number, then its MOE will also be a number; if the estimate value is expressed as a percent, then its MOE will also be a percent. The user should also note that for relatively small geographic areas, such as census tracts shown here, ACS only releases combined 5-year estimates, meaning these estimates represent rolling averages of survey results that were collected over a 5-year span (in this case 2019-2023). Therefore, these data do not represent any one specific point in time or even one specific year. For geographic areas with larger populations, 3-year and 1-year estimates are also available. For further explanation of ACS estimates and margin of error, visit Census ACS website.Source: U.S. Census Bureau, Atlanta Regional CommissionDate: 2019-2023Open Data License: Creative Commons Attribution 4.0 International (CC by 4.0)Link to the data manifest: https://opendata.atlantaregional.com/documents/182e6fcf8201449086b95adf39471831/about

Data set of a community based cross-sectional survey done to find the prevalence , its correlates and patterns in a population of a district in southern Kerala, IndiaBackground: Multi-morbidity is the coexistence of multiple chronic conditions in the same individual. With advancing epidemiological and demographic transitions, the burden of multi-morbidity is expected to increase India. The state of Kerala in India is also in an advanced phase of epidemiological transition. However, very limited data on prevalence of multi-morbidity are available in the Kerala population.

Methods: A cross sectional survey was conducted among 410 participants in the age group of 30-69 years. A multi-stage cluster sampling method was employed to identify the study participants. Every eligible participant in the household were interviewed to assess the household prevalence. A structured interview schedule was used to assess socio-demographic variables, behavioral risk factors and prevailing clinical conditions, PHQ-9 questionnaire for screening of depression and active measurement of blood sugar and blood pressure. Co-existence of two or more conditions out of 11 was used as multi-morbidity case definition. Bivariate analyses were done to understand the association between socio-demographic factors and multi-morbidity. Logistic regression analyses were performed to estimate the effect size of these variables on multi-morbidity.

Results: Overall, the prevalence of multi-morbidity was 45.4% (95% CI: 40.5-50.3%). Nearly a quarter of study participants (25.4%) reported only one chronic condition (21.3-29.9%). Further, 30.7% (26.3-35.5), 10.7% (7.9-14.2), 3.7% (2.1-6.0) and 0.2% reported two, three, four and five chronic conditions, respectively. Nearly seven out of ten households (72%, 95%CI: 65-78%) had at least one person in the household with multi-morbidity and one in five households (22%, 95%CI: 16.7-28.9%) had more than one person with multi-morbidity. With every year increase in age, the propensity for multi-morbidity increased by 10 percent (OR=1.1; 95% CI: 1.1-1.2). Males and participants with low levels of education were less likely to suffer from multi-morbidity while unemployed and who do recommended level of physical activity were significantly more likely to suffer from multi-morbidity. Diabetes and hypertension was the most frequent dyad.

Conclusion: One of two participants in the productive age group of 30-69 years report multi-morbidity. Further, seven of ten households have at least one person with multi-morbidity. Preventive and management guidelines for chronic non-communicable conditions should focus on multi-morbidity especially in the older age group. Health-care systems that function within the limits of vertical disease management and episodic care (e.g., maternal health, tuberculosis, malaria, cardiovascular disease, mental health etc.) require optimal re-organization and horizontal integration of care across disease domains in managing people with multiple chronic conditions.

Key words: Multi-morbidity, cross-sectional, household, active measurement, rural, India, pattern

Abstract

The Thai Demographic and Health Survey (TDHS) was a nationally representative sample survey conducted from March through June 1988 to collect data on fertility, family planning, and child and maternal health. A total of 9,045 households and 6,775 ever-married women aged 15 to 49 were interviewed. Thai Demographic and Health Survey (TDHS) is carried out by the Institute of Population Studies (IPS) of Chulalongkorn University with the financial support from USAID through the Institute for Resource Development (IRD) at Westinghouse. The Institute of Population Studies was responsible for the overall implementation of the survey including sample design, preparation of field work, data collection and processing, and analysis of data. IPS has made available its personnel and office facilities to the project throughout the project duration. It serves as the headquarters for the survey.

The Thai Demographic and Health Survey (TDHS) was undertaken for the main purpose of providing data concerning fertility, family planning and maternal and child health to program managers and policy makers to facilitate their evaluation and planning of programs, and to population and health researchers to assist in their efforts to document and analyze the demographic and health situation. It is intended to provide information both on topics for which comparable data is not available from previous nationally representative surveys as well as to update trends with respect to a number of indicators available from previous surveys, in particular the Longitudinal Study of Social Economic and Demographic Change in 1969-73, the Survey of Fertility in Thailand in 1975, the National Survey of Family Planning Practices, Fertility and Mortality in 1979, and the three Contraceptive Prevalence Surveys in 1978/79, 1981 and 1984.

Geographic coverage

National

Analysis unit

• Household
• Women age 15-49

Universe

The population covered by the 1987 THADHS is defined as the universe of all women Ever-married women in the reproductive ages (i.e., women 15-49). This covered women in private households on the basis of a de facto coverage definition. Visitors and usual residents who were in the household the night before the first visit or before any subsequent visit during the few days the interviewing team was in the area were eligible. Excluded were the small number of married women aged under 15 and women not present in private households.

Kind of data

Sample survey data

Sampling procedure

SAMPLE SIZE AND ALLOCATION

The objective of the survey was to provide reliable estimates for major domains of the country. This consisted of two overlapping sets of reporting domains: (a) Five regions of the country namely Bangkok, north, northeast, central region (excluding Bangkok), and south; (b) Bangkok versus all provincial urban and all rural areas of the country. These requirements could be met by defining six non-overlapping sampling domains (Bangkok, provincial urban, and rural areas of each of the remaining 4 regions), and allocating approximately equal sample sizes to them. On the basis of past experience, available budget and overall reporting requirement, the target sample size was fixed at 7,000 interviews of ever-married women aged 15-49, expected to be found in around 9,000 households. Table A.I shows the actual number of households as well as eligible women selected and interviewed, by sampling domain (see Table i.I for reporting domains).

THE FRAME AND SAMPLE SELECTION

The frame for selecting the sample for urban areas, was provided by the National Statistical Office of Thailand and by the Ministry of the Interior for rural areas. It consisted of information on population size of various levels of administrative and census units, down to blocks in urban areas and villages in rural areas. The frame also included adequate maps and descriptions to identify these units. The extent to which the data were up-to-date as well as the quality of the data varied somewhat in different parts of the frame. Basically, the multi-stage stratified sampling design involved the following procedure. A specified number of sample areas were selected systematically from geographically/administratively ordered lists with probabilities proportional to the best available measure of size (PPS). Within selected areas (blocks or villages) new lists of households were prepared and systematic samples of households were selected. In principle, the sampling interval for the selection of households from lists was determined so as to yield a self weighting sample of households within each domain. However, in the absence of good measures of population size for all areas, these sampling intervals often required adjustments in the interest of controlling the size of the resulting sample. Variations in selection probabilities introduced due to such adjustment, where required, were compensated for by appropriate weighting of sample cases at the tabulation stage.

SAMPLE OUTCOME

The final sample of households was selected from lists prepared in the sample areas. The time interval between household listing and enumeration was generally very short, except to some extent in Bangkok where the listing itself took more time. In principle, the units of listing were the same as the ultimate units of sampling, namely households. However in a small proportion of cases, the former differed from the latter in several respects, identified at the stage of final enumeration: a) Some units listed actually contained more than one household each b) Some units were "blanks", that is, were demolished or not found to contain any eligible households at the time of enumeration. c) Some units were doubtful cases in as much as the household was reported as "not found" by the interviewer, but may in fact have existed.

Mode of data collection

Face-to-face

Research instrument

The DHS core questionnaires (Household, Eligible Women Respondent, and Community) were translated into Thai. A number of modifications were made largely to adapt them for use with an ever- married woman sample and to add a number of questions in areas that are of special interest to the Thai investigators but which were not covered in the standard core. Examples of such modifications included adding marital status and educational attainment to the household schedule, elaboration on questions in the individual questionnaire on educational attainment to take account of changes in the educational system during recent years, elaboration on questions on postnuptial residence, and adaptation of the questionnaire to take into account that only ever-married women are being interviewed rather than all women. More generally, attention was given to the wording of questions in Thai to ensure that the intent of the original English-language version was preserved.

a) Household questionnaire

The household questionnaire was used to list every member of the household who usually lives in the household and as well as visitors who slept in the household the night before the interviewer's visit. Information contained in the household questionnaire are age, sex, marital status, and education for each member (the last two items were asked only to members aged 13 and over). The head of the household or the spouse of the head of the household was the preferred respondent for the household questionnaire. However, if neither was available for interview, any adult member of the household was accepted as the respondent. Information from the household questionnaire was used to identify eligible women for the individual interview. To be eligible, a respondent had to be an ever-married woman aged 15-49 years old who had slept in the household 'the previous night'.

Prior evidence has indicated that when asked about current age, Thais are as likely to report age at next birthday as age at last birthday (the usual demographic definition of age). Since the birth date of each household number was not asked in the household questionnaire, it was not possible to calculate age at last birthday from the birthdate. Therefore a special procedure was followed to ensure that eligible women just under the higher boundary for eligible ages (i.e. 49 years old) were not mistakenly excluded from the eligible woman sample because of an overstated age. Ever-married women whose reported age was between 50-52 years old and who slept in the household the night before birthdate of the woman, it was discovered that these women (or any others being interviewed) were not actually within the eligible age range of 15-49, the interview was terminated and the case disqualified. This attempt recovered 69 eligible women who otherwise would have been missed because their reported age was over 50 years old or over.

b) Individual questionnaire

The questionnaire administered to eligible women was based on the DHS Model A Questionnaire for high contraceptive prevalence countries. The individual questionnaire has 8 sections: - Respondent's background - Reproduction - Contraception - Health and breastfeeding - Marriage - Fertility preference - Husband's background and woman's work - Heights and weights of children and mothers

The questionnaire was modified to suit the Thai context. As noted above, several questions were added to the standard DHS core questionnaire not only to meet the interest of IPS researchers hut also because of their relevance to the current demographic situation in Thailand. The supplemental questions are marked with an asterisk in the individual questionnaire. Questions concerning the following items were added in the individual questionnaire: - Did the respondent ever

In outbred sexually reproducing populations, age-specific mortality rates reach a plateau in late life following the exponential increase in mortality rates that marks aging. Little is known about what happens to physiology when cohorts transition from aging to late life. We measured age-specific values for starvation resistance, desiccation resistance, time-in-motion and geotaxis in ten Drosophila melanogaster populations: five populations selected for rapid development and five control populations. Adulthood was divided into two stages, the aging phase and the late-life phase according to demographic data. Consistent with previous studies, we found that populations selected for rapid development entered the late-life phase at an earlier age than the controls. Age-specific rates of change for all physiological phenotypes showed differences between the aging phase and the late-life phase. This result suggests that late life is physiologically distinct from aging. The ages of transitions in physiological characteristics from aging to late life statistically match the age at which the demographic transition from aging to late life occurs, in all cases but one. These experimental results support evolutionary theories of late life that depend on patterns of decline and stabilization in the forces of natural selection.

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

dataDroso - census dataDemographic transitions of Drosophyllum lusitanicum populations recorded in annual censuses (from 2011 to 2015) in five populations. These data are used to quantify vital rates of above-ground individuals.dataDroso.csvdataDrosoSB - seed bankSeed fates (in a binary format) inferred from two experiments. These data are used to quantify the transitions related to the seed bank and associated parameter uncertainties.dataDrosoSB.csvBayModel - Bayesian vital rate GLMMsExecutes and saves the results of a Bayesian model quantifying all vital rates; illustrates basic diagnostics that can be run on the results of an MCMC run (i.e., the posterior parameter distribution) to check for model convergence and autocorrelation of the posterior samples.BayModel.RmcmcOUT - parameter samplesIn case the reader wishes to forego the step of fitting the Bayesian models, we provided a mcmcOUT.csv file with 1000 posterior parameter values for each of the parameters estimated with Bayesian m...

While the BRICS countries are grouped together in terms of economic development, demographic progress varies across these five countries. In 2019, India and South Africa were the only BRICS countries with a fertility rate above replacement level (2.1 births per woman). Fertility rates since 2000 show that fertility in China and Russia has either fluctuated or remained fairly steady, as these two countries are at a later stage of the demographic transition than the other three, while Brazil has reached this stage more recently. Fertility rates in India are following a similar trend to Brazil, while South Africa's rate is progressing at a much slower pace. Demographic development is inextricably linked with economic growth; for example, as fertility rates drop, female participation in the workforce increases, as does the average age, which then leads to higher productivity and a more profitable domestic market.

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.

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

Demographic processes that ensure the recovery and resilience of marine populations are critical as climate change sends an increasing proportion on a trajectory of decline. Yet for some populations, recovery potential remains high. We conducted annual monitoring over 9-years (2012–2020) to assess the recovery of coral populations belonging to genus Pocillopora. These populations experienced a catastrophic collapse following a severe typhoon in 2009. From the start of the monitoring period, high initial recruitment led to the establishment of a juvenile population that rapidly transitioned to sexually mature adults, which dominated the population within six years after the disturbance. As a result, coral cover increased from 1.1% to 20.2% during this time. To identify key demographic drivers of recovery and population growth rates (λ), we applied kernel resampled Integral Projection Models (IPMs), constructing eight successive models to examine annual change. IPMs were able to capture reproductive traits as key demographic drivers over the initial 3 years, whilst individual growth was a continuous key demographic driver throughout the entire monitoring period. IPMs further detected a pulse of reproductive output subsequent to two further Category 5 typhoon events during the monitoring period, exemplifying key mechanisms of resilience for coral populations impacted by disturbance. Despite rapid recovery, (i.e., increased coral cover, individual colony growth, low mortality), IPMs estimated predominantly negative values of λ, indicating a declining population. Indeed, whilst λ translates to a change in the number of individuals, the recovery of coral populations can also be driven by an increase in the size of coral surviving colonies. Our results illustrate that accumulating long-term data of historical dynamics and applying IPMs to extract demographic drivers are crucial for future predictions that are based on comprehensive and robust understandings of ecological change. Methods Data collection Orchid Island (22°03′N, 121°32′E) is a 45 km2 volcanic, tropical island 64 km off the coast of Taiwan, encircled by a narrow fringing reef (5–10 m depth), leading to a dramatic drop-off. Such reef topography is sensitive to typhoons that are both frequent and intense in the region (Ribas-Deulofeu et al., 2021). In 2009, the island was severely affected by Typhoon Morakot (Hall et al., 2013), the deadliest typhoon to hit Taiwan in recorded history, which caused a ~66% decline in mean live coral cover (~60% to ~20%) along reefs in southern Taiwan (Kuo et al., 2011). Three years after this major disturbance in 2012, three parallel 20 m transects were established at ~8 m depth spaced ~2.5 m apart at a site to the southwest of the island (named Green Grassland; 22°00'N 121°34'E). Usually, this reef site is relatively sheltered from both the waves generated by the winter north-easterly monsoon and summer south-westerly winds. However, on this occasion was proven susceptible to the typhoon in 2009, impacted by a west to south-westerly swell. To the side of each transect, 50 cm × 50 cm permanent quadrats (n = 11) were haphazardly positioned to assess demographic changes over time (Appendix S1: Figure S1 and Figure S2). A total of 33 quadrats were made permanent by placing markers (iron pegs) at each corner with tags attached indicating the designated identification number, making the quadrat easier to detect for future monitoring. With this method, only four quadrats were lost in subsequent surveys, whereas the position of the other quadrats (n = 29) remained. However, the precise location may slightly vary due to the ever-changing dynamics of the reef. For this reason, when quadrats were placed at each marked position, a wide-scale community picture was taken ca. 2 m above the substrate, in order to correct any error in positioning during the analysis, if necessary (Appendix S1: Figure S1a). The quadrat was then photographed at a higher-resolution ca. 1 m above the substrate in order to capture the overall benthic composition (Appendix S1: Figure S1b). The quadrat was then divided into four sections, which were individually captured in order to attain high-resolution images of individual colonies (Appendix S1: Figure S1c). In each of these sections, smaller sized individuals (ca. < 5 cm in length) were photographed with close-up images and scale. This protocol was repeated annually between 2012 and 2020. From photographs of the 29 permanent quadrats, every colony (n = 336) observed was first measured for its two-dimensional (2D) colony size (projected area) using Image J software (Schneider, Rasband and Eliceiri, 2012). The 2D colony size was used for the analysis of annual change in coral cover. Coral cover (%) was determined by measuring the sum of 2D projected areas of coral colonies relative to the area of all quadrats combined. All quadrats were originally occupied by Pocillopora, but from 2016 to the end of the monitoring period, 1 quadrat was empty of Pocillopora colonies with the cover calculation still taking this area into consideration. In the rare case of a slight overlap of colonies (n = 4), the 2D projected area could be easily deduced for the unseen part of colonies. Measurement of demographic vital rates In this study, we focused on locally dominant genus Pocillopora spp. The relative contribution of species to the Pocillopora complex was genetically examined by randomly sampling colonies at the site and barcoding mtORF region after extraction of genomic DNA (Johnston, Forsman and Toonen, 2018). Out of 31 sampled Pocillopora colonies, 17 were P. verrucosa and 14 were P. meandrina (Appendix S1: Table S1 and S2). Besides the two dominant Pocillopora species, there were at least two other Pocillopora species present; Pocillopora eydouxi and Pocillopora sp. These species are broadcast-spawners, with the exception of Pocillopora sp., which is a brooder (Mulla et al. 2021). Due to the difficulty in identifying species morphologically in the field, especially at the early life stages, we treated species as a Pocillopora complex (Pocillopora populations). As corals are 3D structures, colony size (surface area) was used for Pocillopora colonies in the IPMs, which allowed us to build higher-resolution models. 3D surface area (cm2) was allometric and estimated from 2D projections using a pre-established relationship. Detailed information on the 2D to 3D conversion can be found in Appendix Figure S3. We extracted information on colony growth, survival and recruitment of Pocillopora populations over the 9-year period using size-thresholds in 3D to distinguish visible recruits (0.4–10 cm2; n = 154), juveniles (10.1–100 cm2; n = 369) and adults (> 100.1 cm2; n = 532). These threshold for visible recruits was determined from the size range of newly appearing individuals from each year from the second year of monitoring. The threshold for juveniles was determined by the maximum size of visible recruits and the minimum size of sexually mature individuals (described in more detail below). These thresholds differentiate sexually immature (visible recruits/juveniles) to mature (adults) individuals, used for ecological interpretation. To identify size-specific relationships of demographic traits associated with reproduction, two nubbins (~5 cm in branch length) were collected from 40 colonies of varying size of P. verrucosa (probably including P. meandrina: 68.2–685.8 cm2 in 3D size) during the reproductive season (April, 2017) at neighbouring Green Island (Lin and Nozawa, 2017). In addition, a further 20 nubbins (of the same size) were collected (68.2–364.7 cm2 in 3D size) to determine the minimum size of sexual maturity at the same time. Nubbins were fixed in a 10% formalin-seawater solution and examined using standard histological methods. Tissue of nubbins were decalcified and dehydrated in an alcohol series using a tissue processor (Thermo Scientific, Excelsior ES, USA) and embedded in paraffin wax (Thermo Scientific Histoplast PE, USA). Samples were then cut with a microtome (Thermo Scientific, Finesse 325) at 6 µm thick intervals. Xylene was used to deparaffin samples and tissue sections were mounted on glass slides, stained with hematoxylin and eosin using a staining machine (Shandon Varistain, Thermo Scientific, USA) and then preserved with Organol/Limonene mounting medium and a glass cover. Sections were examined under a BX51 light microscope (Olympus, Japan). For each nubbin, 2 polyps were haphazardly chosen and the number of oocytes per polyp was determined by observing the entire section of each polyp (a total of 4 polyps per colony). The probability of a colony being reproductively active was determined by the presence or absence of oocytes over colony size.

Data collection from official statistics and church registers