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

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 reintroduction of rare species in natural preserves is a commonly used restoration strategy to prevent species extinction. An essential first step in planning successful reintroductions is identifying which life stages (e.g., seeds or large adults) should be used to establish these new populations. Following this initial establishment phase, it is necessary to determine the level of survival, growth, and recruitment needed to maintain population persistence over time and identify management actions that will achieve these goals. In this 5-year study, we projected the short- and long-term population growth rates of a critically endangered long-lived shrub, Delissea waianaeensis. Using this model system, we show that reintroductions established with mature individuals have the lowest probability of quasi-population extinction (10 individuals) and the highest increase in population abundance. However, our results also demonstrate that short-term increases in population abundances are overly optimistic of long-term outcomes. Using long-term stochastic model simulations, we identified the level of natural seedling regeneration needed to maintain a positive population growth rate over time. These findings are relevant for planning future reintroduction efforts for long-lived species and illustrate the need to forecast short- and long-term population responses when evaluating restoration success.

Species distribution models (SDMs) are widely used to infer species-environment relationships, predict spatial distributions, and characterise species’ environmental niches. While the importance of space and spatial scales is widely acknowledged in SDM applications, temporal components of the niche are rarely addressed. We discuss how phenology and demographic stages affect model inference in plant SDMs. Ignoring conspicuousness and timing of phenological stages may bias niche estimates through increased observer bias, while ignoring stand age may bias niche estimates through temporal mismatches with environmental variables, especially during times of rapid global warming. We present different methods to consider phenology and demographic stages in plant SDMs, including the selection of causal, spatiotemporally explicit predictors, and the calibration of stage-specific SDMs. Based on a case study with citizen science data, we illustrate how spatiotemporal SDMs provide deeper insights on..., We conducted a keyword-based search in the Web of Science to quantify how often temporal components related to phenology and demographic stages are explicitly considered in plant SDMs. A full list of keywords is provided in the Supporting Information Table S1. We used a nested set of keywords to identify all studies that mentioned SDMs (or common synonyms), were focused on plants, and were listing relevant keywords related to phenology or to demographic stages, respectively. The search was carried out on 5-Oct-2023 and was restricted to English-language journal articles in the period 1945-2022 (no studies using SDMs were published before that start year). Overall, we found more than 40,000 articles mentioning SDM and over 10,000 articles in our refined search for plant SDMs, with a strong increase in the number of articles over time. Among these, phenology (or related search terms) was mentioned in 970 articles and demographic stages (or related terms) in 1188 articles, each averaging c..., , # The niche through time: considering phenology and demographic stages in plant distribution models

https://doi.org/10.5061/dryad.sn02v6xct

Description of the data and file structure

Columns from WoS (Web of Science) search – these are identical in both excel sheets

These columns are the standard columns provided as WoS search output. If the entries contain "n/a", then no information was provided by WoS because those items are not applicable. For example, a journal article does not have any entries for book authors.

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

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.

Life Table Data: Field-based, partial life table data for immature stages of Bemisia tabaci on cotton in Maricopa, Arizona, USA. Data were generated on approximately 200 individual insects per cohort with 2-5 cohorts per year for a total of 44 cohorts between 1997 and 2010. Data provide the marginal, stage-specific rates of mortality for eggs, and 1st, 2nd, 3rd, and 4th instar nymphs. Mortality is characterized as caused by inviability (eggs only), dislodgement, predation, parasitism and unknown. Detailed methods can be found in Naranjo and Ellsworth 2005 (Entomologia Experimentalis et Applicata 116(2): 93-108). The method takes advantage of the sessile nature of immature stages of this insect. Briefly, an observer follows individual eggs or settled first instar nymphs from natural populations on the underside of cotton leaves in the field with a hand lens and determines causes of death for each individual over time. Approximately 200 individual eggs and nymphs are observed for each cohort. Separately, densities of eggs and nymphs are monitored with standard methods (Naranjo and Flint 1994, Environmental Entomology 23: 254-266; Naranjo and Flint 1995, Environmental Entomology 24: 261-270) on a weekly basis.
Matrix Model Data: Life table data were used to provide parameters for population matrix models. Matrix models contain information about stage-specific rates for development, survival and reproduction. The model can be used to estimate overall population growth rate and can also be analyzed to determine which life stages contribute the most to changes in growth rates. Resources in this dataset:Resource Title: Matrix model data from Naranjo, S.E. (2017) Retrospective analysis of a classical biological control program. Journal of Applied Ecology. File Name: MatrixModelData.xlsxResource Description: Life table data were used to provide parameters for population matrix models. Matrix models contain information about stage-specific rates for development, survival and reproduction. The model can be used to estimate overall population growth rate and can also be analyzed to determine which life stages contribute the most to changes in growth rates. Resource Title: Data Dictionary: Life table data. File Name: DataDictionary_LifeTableData.csvResource Title: Life table data from Naranjo, S.E. (2017) Retrospective analysis of a classical biological control program. Journal of Applied Ecology. File Name: LifeTableData.xlsxResource Description: Field-based, partial life table data for immature stages of Bemisia tabaci on cotton in Maricopa, Arizona, USA. Data were generated on approximately 200 individual insects per cohort with 2-5 cohorts per years for a total of 44 cohorts between 1997 and 2010. Data provide the marginal, stage-specific rates of mortality for eggs, and 1st, 2nd, 3rd, and 4th instar nymphs. Mortality is characterized as caused by inviability (eggs only), dislodgement, predation, parasitism and unknown. Detailed methods can be found in Naranjo and Ellsworth 2005 (Entomologia, Experimentalis et Applicata 116: 93-108). The method takes advantage of the sessile nature of immature stages of this insect. Briefly, an observer follows individual eggs or settled first instar nymphs from natural populations on the underside of cotton leaves in the field with a hand lens and determines causes of death for each individual over time. Approximately 200 individual eggs and nymphs are observed for each cohort. Separately, densities of eggs and nymphs are monitored with standard methods (Naranjo and Flint 1994, Environmental Entomology 23: 254-266; Naranjo and Flint 1995, Environmental Entomology 24: 261-270) on a weekly basis. Resource Title: Life table data from Naranjo, S.E. (2017) Retrospective analysis of a classical biological control program. Journal of Applied Ecology. File Name: LifeTableData.csvResource Description: CSV version of the data. Field-based, partial life table data for immature stages of Bemisia tabaci on cotton in Maricopa, Arizona, USA. Data were generated on approximately 200 individual insects per cohort with 2-5 cohorts per years for a total of 44 cohorts between 1997 and 2010. Data provide the marginal, stage-specific rates of mortality for eggs, and 1st, 2nd, 3rd, and 4th instar nymphs. Mortality is characterized as caused by inviability (eggs only), dislodgement, predation, parasitism and unknown. Detailed methods can be found in Naranjo and Ellsworth 2005 (Entomologia, Experimentalis et Applicata 116: 93-108). The method takes advantage of the sessile nature of immature stages of this insect. Briefly, an observer follows individual eggs or settled first instar nymphs from natural populations on the underside of cotton leaves in the field with a hand lens and determines causes of death for each individual over time. Approximately 200 individual eggs and nymphs are observed for each cohort. Separately, densities of eggs and nymphs are monitored with standard methods (Naranjo and Flint 1994, Environmental Entomology 23: 254-266; Naranjo and Flint 1995, Environmental Entomology 24: 261-270) on a weekly basis.

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

Introduction

This report presents projections of population from 2015 to 2025 by age and sex for Illinois, Chicago and Illinois counties produced for the Certificate of Need (CON) Program. As actual future population trends are unknown, the projected numbers should not be considered a precise prediction of the future population; rather, these projections, calculated under a specific set of assumptions, indicate the levels of population that would result if our assumptions about each population component (births, deaths and net migration) hold true. The assumptions used in this report, and the details presented below, generally assume a continuation of current trends.

Methodology These projections were produced using a demographic cohort-component projection model. In this model, each component of population change – birth, death and net migration – is projected separately for each five-year birth cohort and sex. The cohort – component method employs the following basic demographic balancing equation: P1 = P0 + B – D + NM Where: P1 = Population at the end of the period; P0 = Population at the beginning of the period; B = Resident births during the period; D = Resident deaths during the period; and NM = Net migration (Inmigration – Outmigration) during the period. The model roughly works as follows: for every five-year projection period, the base population, disaggregated by five-year age groups and sex, is “survived” to the next five-year period by applying the appropriate survival rates for each age and sex group; next, net migrants by age and sex are added to the survived population. The population under 5 years of age is generated by applying age specific birth rates to the survived females in childbearing age (15 to 49 years).

Base Population These projections began with the July 1, 2010 population estimates by age and sex produced by the U.S. Census Bureau. The most recent census population of April 1, 2010 was the base for July 1, 2010 population estimates.

Special Populations In 19 counties, the college dormitory population or adult inmates in correctional facilities accounted for 5 percent or more of the total population of the county; these counties were considered as special counties. There were six college dorm counties (Champaign, Coles, DeKalb, Jackson, McDonough and McLean) and 13 correctional facilities counties (Bond, Brown, Crawford, Fayette, Fulton, Jefferson, Johnson, Lawrence, Lee, Logan, Montgomery, Perry and Randolph) that qualified as special counties. When projecting the population, these special populations were first subtracted from the base populations for each special county; then they were added back to the projected population to produce the total population projections by age and sex. The base special population by age and sex from the 2010 population census was used for this purpose with the assumption that this population will remain the same throughout each projection period.

Mortality Future deaths were projected by applying age and sex specific survival rates to each age and sex specific base population. The assumptions on survival rates were developed on the basis of trends of mortality rates in the individual life tables constructed for each level of geography for 1989-1991, 1999-2001 and 2009-2011. The application of five-year survival rates provides a projection of the number of persons from the initial population expected to be alive in five years. Resident deaths data by age and sex from 1989 to 2011 were provided by the Illinois Center for Health Statistics (ICHS), Illinois Department of Public Health.

Fertility Total fertility rates (TFRs) were first computed for each county. For most counties, the projected 2015 TFRs were computed as the average of the 2000 and 2010 TFRs. 2010 or 2015 rates were retained for 2020 projections, depending on the birth trend of each county. The age-specific birth rates (ASBR) were next computed for each county by multiplying the 2010 ASBR by each projected TFR. Total births were then projected for each county by applying age-specific birth rates to the projected female population of reproductive ages (15 to 49 years). The total births were broken down by sex, using an assumed sex-ratio at birth. These births were survived five years applying assumed survival ratios to get the projected population for the age group 0-4. For the special counties, special populations by age and sex were taken out before computing age-specific birth rates. The resident birth data used to compute age-specific birth rates for 1989-1991, 1999-2001 and 2009-2011 came from ICHS. Births to females younger than 15 years of age were added to those of the 15-19 age group and births to women older than 49 years of age were added to the 45-49 age group.

Net Migration Migration is the major component of population change in Illinois, Chicago and Illinois counties. The state is experiencing a significant loss of population through internal (domestic migration within the U.S.) net migration. Unlike data on births and deaths, migration data based on administrative records are not available on a regular basis. Most data on migration are collected through surveys or indirectly from administrative records (IRS individual tax returns). For this report, net migration trends have been reviewed using data from different sources and methods (such as residual method) from the University of Wisconsin, Madison, Illinois Department of Public Health, individual exemptions data from the Internal Revenue Service, and survey data from the U.S. Census Bureau. On the basis of knowledge gained through this review and of levels of net migration from different sources, assumptions have been made that Illinois will have annual net migrants of -40, 000, -35,000 and -30,000 during 2010-2015, 2015-2020 and 2020-2025, respectively. These figures have been distributed among the counties, using age and sex distribution of net migrants during 1995-2000. The 2000 population census was the last decennial census, which included the question “Where did you live five years ago?” The age and sex distribution of the net migrants was derived, using answers to this question. The net migration for Chicago has been derived independently, using census survival method for 1990-2000 and 2000-2010 under the assumption that the annual net migration for Chicago will be -40,000, -30,000 and -25,000 for 2010-2015, 2015-2020 and 2020-2025, respectively. The age and sex distribution from the 2000-2010 net migration was used to distribute the net migrants for the projection periods.

Conclusion These projections were prepared for use by the Certificate of Need (CON) Program; they are produced using evidence-based techniques, reasonable assumptions and the best available input data. However, as assumptions of future demographic trends may contain errors, the resulting projections are unlikely to be free of errors. In general, projections of small areas are less reliable than those for larger areas, and the farther in the future projections are made, the less reliable they may become. When possible, these projections should be regularly reviewed and updated, using more recent birth, death and migration data.

Footnotes: 1 Population estimates based on the Standard Geographical Classification (SGC) 2016 as delineated in the 2016 Census. 2 A census metropolitan area (CMA) or a census agglomeration (CA) is formed by one or more adjacent municipalities centred on a population centre (known as the core). A CMA must have a total population of at least 100,000 of which 50,000 or more must live in the core based on adjusted data from the previous Census of Population Program. A CA must have a core population of at least 10,000 also based on data from the previous Census of Population Program. To be included in the CMA or CA, other adjacent municipalities must have a high degree of integration with the core, as measured by commuting flows derived from data on place of work from the previous Census Program. If the population of the core of a CA falls below 10,000, the CA is retired from the next census. However, once an area becomes a CMA, it is retained as a CMA even if its total population declines below 100,000 or the population of its core falls below 50,000. All areas inside the CMA or CA that are not population centres are rural areas. When a CA has a core of at least 50,000, based on data from the previous Census of Population, it is subdivided into census tracts. Census tracts are maintained for the CA even if the population of the core subsequently falls below 50,000. All CMAs are subdivided into census tracts (2016 Census Dictionary, catalogue number 98-301-X2016001). 3 An area outside census metropolitan areas and census agglomerations is made up of all areas (within a province or territory) unallocated to a census metropolitan area (CMA) or census agglomeration (CA). 4 The population growth, which is used to calculate population estimates of census metropolitan areas and census agglomerations (table 17100135), is comprised of the components of population growth (table 17100136). 5 This table replaces table 17100079. 6 The components of population growth for census metropolitan areas (CMAs) and census agglomerations (CAs) sometimes had to be calculated using information at the census division level, using the geographic conversion method. This method involves using the population component calculated at the level of the CD(s) in which the CMA or CA is located and applying a ratio corresponding to the proportion of the CMA or CA population included in the corresponding CD(s). For periods prior to 2005/2006, all demographic components for all CMAs and CAs were calculated using geographic conversions. For the periods from 2005/2006 to 2010/2011 inclusively, emigration and internal migration components for areas that were not CMAs according to the 2011 SGC were calculated using geographic conversions. For the periods 2011/2012 to 2015/2016 inclusively, the emigration and internal migration components of regions that were not CMAs or CAs according to the 2011 SGC were calculated using geographic conversions. For the relevant demographic components, trends should be interpreted with caution where the method of calculation has changed over time. This caveat applies particularly to the intraprovincial migration component, for which the assumptions of the geographic conversion method are more at risk of not being met. 7 Period from July 1 to June 30. 8 Age on July 1. 9 The estimates for deaths are preliminary for 2020/2021, updated for 2019/2020 and final up to 2018/2019. Preliminary and updated estimates of deaths were produced by Demography Division, Statistics Canada (see definitions, data sources and methods record number 3601 and 3608) with the exception of Quebec's data which are taken from the estimates of "l'Institut de la statistique du Québec" (ISQ) and then adjusted to Statistics Canada's provincial estimates. Final data were produced by Health Statistics Division Statistics Canada (see definitions data sources and methods record number 3233). However before 2011 the final estimates may differ from the data released by the Health Statistics Division due to the imputation of certain unknown values. In addition for estimates of deaths the age represents age at the beginning of the period (July 1st) and not the age at the time of occurrence as with the Health Statistics Division data."

SummaryWe modified a bi-seasonal Ricker model previously developed by Betini et al. (2013) to examine the effects of season-specific habitat loss in either the breeding or non-breeding period and different strengths of density dependence on the production of experimentally-derived signals of population decline. The bi-seasonal habitat loss model is parameterized using the r-K formulation of the Ricker model, with separate values of growth rate (r) and carrying capacity (K) for each season (i.e., rb = reproductive output, rnb = non-breeding mortality, Kb = carrying capacity in the breeding period, Knb = carrying capacity in the non-breeding period). Exponential habitat decay is simulated in either season using two additional terms: Hb (the proportion of initial food remaining in the breeding period) and Hnb (the proportion of initial food remaining in the non-breeding period). The code here is used to simulate five different rates of habitat loss in either the breeding or non-breeding period over breeding or non-breeding of 50 generations, with habitat loss commencing after 20 generations. We ran 1,000 replicates simulations for each scenario/parameterization (see below). Initial starting parameters for a particular simulation are sampled from a distribution to allow for some degree of variability (but not strictly stochasticity) in population dynamics. We randomly sampled 25 replicates from each parameterization for subsequent plotting and analysis, data from which are provided in the CSV file.A complete description of the simulation methods and analysis is available in the pre-print on EcoEvoRxiv.Contentsbiseasonal_Ricker_code.R — R code to produce a bi-seasonal Ricker model in which habitat loss is simulated in either the breeding or non-breeding period.biseasonal_Ricker_simdata.csv — a sample of 25 simulated time series of bi-seasonal population abundance under different seasons and rates of habitat loss and strengths of density dependence.Variable definitionsnitt_t_DD = unique replicate identifier (factor) combining the replicate number (nitt), treatment type (t), and strength of density dependence (DD) simulated (e.g., "14_control_flies" references simulation 14 for the control treatment with the strength of density dependence based on values derived from an experimental population of fruit flies) — see variables below.nitt = replicate identification number (not strictly unique to different treatments)strength_DD = four-level factor (flies, weak, moderate, strong) indicating the initial strength of density dependence used to parameterize the model. In all cases, the strength was the same in both the breeding and non-breeding period (i.e., weak and weak, moderate and moderate, etc.). See values in the methods section of the paper or in the specific code for each model parameterization.treat = 11-level factor (control, b02, b5, b10, b20, b25, n02, n05, n10, n20, n25) indicating the season and rate of habitat loss being simulated where "control" indicates no habitat loss and "bXX" and "nXX" indicate breeding habitat and non-breeding habitat loss, respectively, at 2, 5, 10, 20, or 25% per generation.seasonT = three-level factor (c, b, n) indicating the season of treatment (c = control = no habitat loss, b = breeding, n = non-breeding).lossT = 6-level factor (0, 2, 5, 10, 20, 25) indicating the rate of habitat loss as a percent decrease per generation. A value of zero (0) indicates no habitat loss applied (i.e., for controls).time = integer (range = 1 to 100) indicating the time step in the model. Each generation (see below) consists of two timesteps (one each for the non-breeding and breeding seasons).gen = integer (range = 1 to 50) indicating the generation in each simulation. Each generation is repeated twice (with one row for each season). Each replicate was simulated for 20 generations under control conditions before the commencement of habitat loss in generation 21.season = two-level factor (n = non-breeding, b = breeding) indicating the season within each generation.count = integer value indicating the population size simulated in each season of each generation.rate = continuous value expressing the change in population size from the previous timestep to the current (e.g., if the previous (non-breeding) population size was 189 and the current (breeding) value is 249, then rate = 249/189 = 1.32). For rows where season = b = breeding, this value represents the breeding growth rate; when season = n = non-breeding, this value indicates non-breeding survival.first_match = integer indicates the first timestep in which population size reached zero (0) indicating the season and generation in which a simulation became extinct.ReferencesBetini, G.S., Griswold, C.K., and Norris, D.R. (2013), Carry-over effects, sequential density dependence and the dynamics of populations in a seasonal environment. Proceedings of the Royal Society B 280: 20130110. https://doi.org/10.1098/rspb.2013.0110

About this dataset

Context

The dataset tabulates the data for the Green Level, NC population pyramid, which represents the Green Level population distribution across age and gender, using estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. It lists the male and female population for each age group, along with the total population for those age groups. Higher numbers at the bottom of the table suggest population growth, whereas higher numbers at the top indicate declining birth rates. Furthermore, the dataset can be utilized to understand the youth dependency ratio, old-age dependency ratio, total dependency ratio, and potential support ratio.

Key observations

• Youth dependency ratio, which is the number of children aged 0-14 per 100 persons aged 15-64, for Green Level, NC, is 39.2.
• Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for Green Level, NC, is 28.9.
• Total dependency ratio for Green Level, NC is 68.0.
• Potential support ratio, which is the number of youth (working age population) per elderly, for Green Level, NC is 3.5.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group for the Green Level population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Green Level for the selected age group is shown in the following column.
• Population (Female): The female population in the Green Level for the selected age group is shown in the following column.
• Total Population: The total population of the Green Level for the selected age group is shown in the following column.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Green Level Population by Age. You can refer the same here

The COVID-19 pandemic increased the global death rate, reaching *** in 2021, but had little to no significant impact on birth rates, causing population growth to dip slightly. On a global level, population growth is determined by the difference between the birth and death rates, known as the rate of natural change. On a national or regional level, migration also affects population change. Ongoing trends Since the middle of the 20th century, the global birth rate has been well above the global death rate; however, the gap between these figures has grown closer in recent years. The death rate is projected to overtake the birth rate in the 2080s, which means that the world's population will then go into decline. In the future, death rates will increase due to ageing populations across the world and a plateau in life expectancy. Why does this change? There are many reasons for the decline in death and birth rates in recent decades. Falling death rates have been driven by a reduction in infant and child mortality, as well as increased life expectancy. Falling birth rates were also driven by the reduction in child mortality, whereby mothers would have fewer children as survival rates rose - other factors include the drop in child marriage, improved contraception access and efficacy, and women choosing to have children later in life.

Footnotes: 1 Population estimates based on the Standard Geographical Classification (SGC) 2016 as delineated in the 2016 Census. 2 A census metropolitan area (CMA) or a census agglomeration (CA) is formed by one or more adjacent municipalities centred on a population centre (known as the core). A CMA must have a total population of at least 100,000 of which 50,000 or more must live in the core based on adjusted data from the previous Census of Population Program. A CA must have a core population of at least 10,000 also based on data from the previous Census of Population Program. To be included in the CMA or CA, other adjacent municipalities must have a high degree of integration with the core, as measured by commuting flows derived from data on place of work from the previous Census Program. If the population of the core of a CA falls below 10,000, the CA is retired from the next census. However, once an area becomes a CMA, it is retained as a CMA even if its total population declines below 100,000 or the population of its core falls below 50,000. All areas inside the CMA or CA that are not population centres are rural areas. When a CA has a core of at least 50,000, based on data from the previous Census of Population, it is subdivided into census tracts. Census tracts are maintained for the CA even if the population of the core subsequently falls below 50,000. All CMAs are subdivided into census tracts (2016 Census Dictionary, catalogue number 98-301-X2016001). 3 An area outside census metropolitan areas and census agglomerations is made up of all areas (within a province or territory) unallocated to a census metropolitan area (CMA) or census agglomeration (CA). 4 The population growth, which is used to calculate population estimates of census metropolitan areas and census agglomerations (table 17100135), is comprised of the components of population growth (table 17100136). 5 This table replaces table 17100079. 6 The components of population growth for census metropolitan areas (CMAs) and census agglomerations (CAs) sometimes had to be calculated using information at the census division level, using the geographic conversion method. This method involves using the population component calculated at the level of the CD(s) in which the CMA or CA is located and applying a ratio corresponding to the proportion of the CMA or CA population included in the corresponding CD(s). For periods prior to 2005/2006, all demographic components for all CMAs and CAs were calculated using geographic conversions. For the periods from 2005/2006 to 2010/2011 inclusively, emigration and internal migration components for areas that were not CMAs according to the 2011 SGC were calculated using geographic conversions. For the periods 2011/2012 to 2015/2016 inclusively, the emigration and internal migration components of regions that were not CMAs or CAs according to the 2011 SGC were calculated using geographic conversions. For the relevant demographic components, trends should be interpreted with caution where the method of calculation has changed over time. This caveat applies particularly to the intraprovincial migration component, for which the assumptions of the geographic conversion method are more at risk of not being met. 7 Period from July 1 to June 30. 8 Age on July 1. 9 The estimates for immigrants are preliminary for 2020/2021 and final up to 2019/2020.

The American black bear (Ursus americanus) has one of the broadest geographic distributions of any mammalian carnivore in North America. Populations occur from high to low elevations and from mesic to arid environments, and their demographic traits have been documented in a wide variety of environments. However, the demography of American black bears in semiarid environments, which comprise a significant portion of the geographic range, is poorly documented. To fill this gap in understanding, we used data from a long-term mark-recapture study of black bears in the semiarid environment of eastern Utah, USA. Cub and yearling survival were low and adult survival was high relative to other populations. Adult life stages had the highest reproductive value, comprised the largest proportion of the population, and exhibited the highest elasticity contribution to the population growth rate (i.e., λ). Vital rates of black bears in this semiarid environment are skewed toward higher survival of adults, and lower survival of cubs compared to other populations. Methods Mark-Recapture study We estimated survival rates from long-term mark-recapture data gathered as part of a 27-year study on American black bears of the East Tavaputs Plateau. During the first 12 years of the study (June to August 1991-2003) female bears were captured and radio-collared, and all bears were tagged in the ear, except for cubs and yearlings. For the entire study (1992 – 2019), collared females were visited in their dens annually during their winter hibernation to count newborn cubs and surviving yearlings. Age of individual bears was determined by 2 methods: (1) direct observation of cubs or yearlings (i.e., year of birth was known) or (2) cementum annuli analysis of a cross-section of the root of an extracted premolar (Palochak, 2004; Willey, 1974). The data we used to derive survival and fecundity rates consisted of the ID_number, cohort (cub, yearling, subadult, prime-aged adult, and old adult), age in years, sex (female, male, unknown), number of cubs, number of yearlings, first observation of individual, last observation of individual, days from last observation, and survival status. We did not include subadult and adult male bears in the analysis. Survival rates To determine the average survival rates for each life stage, we used a Cox proportional hazards model in program R (Team, 2022). This model accommodates staggered entries, where individuals enter the study at different times, and censoring, where the event of interest (e.g., mortality) is not observed for all individuals due to the inability to follow-up or the study ending before the event occurs. These features allow for a more accurate representation of survival over time, even with incomplete data (Cox, 1972). The Cox model is a semi-parametric approach that examines how covariates, such as age and environmental factors, influence the risk of death at any given point in time. Unlike fully parametric models, which require defining the baseline hazard function (the risk of death when all covariates are at baseline levels), the Cox model does not require this step, making it highly flexible and suitable for diverse data and applications (Zhang, 2016). The hazard function in this context refers to the rate or likelihood of an event (e.g., death) occurring at a specific moment, given that the individual has survived up to that time. The Cox model is expressed as follows: h(t|X) = h0(t) exp(β1X1 + β2X2 +...+ βpXp) where h(t|X) is the hazard function at time t given covariates X, h0(t) is the baseline hazard function β1, β2, …, βp are the coefficients for the predictor variables X1, X2, …, Xp. The model assumes proportional hazards, meaning the relative risk of death (the hazard ratio) between two groups remains constant over time (Zhang, 2016). The advantage of the Cox model is its ability to handle censored data, common in survival analysis. Censoring occurs when some individuals have not experienced mortality by the end of the study, so we only know that they survived up to that point. Moreover, the Cox model can incorporate time-dependent covariates, enabling a dynamic analysis of how risk factors influence survival over time (Therneau & Grambsch, 2000). For our analysis, we formulated four Cox proportional hazards models as follows: 1) constant survival, 2) a model with the effect of maternal age, 3) a model with the effect of cohort, and 4) a model with the combined effect of age and cohort. We compared these models using Akaike’s Information Criterion (AIC) to identify the best fit and then evaluate the effect sizes of covariates based on the β coefficients from the top-performing model (Burnham et al., 2011; Symonds & Moussalli, 2011). When there was uncertainty in model selection, we used model averaging to estimate effect sizes and β coefficients. Each model was also checked for uninformative parameters (Arnold, 2010). We reviewed the model summaries to assess the estimated effects of covariates (constant survival, maternal age, cohort, and the combination of age and cohort) on survival outcomes. Fecundity rates To determine fecundity rates, we used females monitored through the use of radio-collars. All females that were ≥ four years old were counted in the breeding pool. We removed any female ≥ 25 years of age from the breeding pool (Noyce, 2010). We classified old adults as ≥ 15 years old and prime-aged adults as 4-14 years of age. We visited dens of females to observe whether they were alone or accompanied by cubs or yearlings as well as the sexes of their offspring. At the height of the study, we had 15 prime-aged adult females, along with a few old-adult females. There was variation in the number of adult females and old-adult females throughout the study period and we had at least two old-adult females in each year for 12 years during the study. Matrix Transition Model and Analysis We developed a transition matrix model based on adult females and their offspring to estimate population growth and additional demographic parameters. In the model, we assumed every cub was born on January 1st and survived through the full year if they were alive through the 15th of October. We assumed density of males does not affect breeding success (Lewis et al., 2014). We divided the population into five age-based stages: cub (0–1 year-old); yearling (1–2 years old), subadult (2–4 years old), prime-aged adult (4–14 years old), and old adult (15+). We used the term sm to indicate the probability of surviving and transitioning to a new stage (matrix sub diagonal), and the term ss indicated the probability of surviving and staying in the same stage (matrix diagonal). We used f to indicate fecundity or reproduction (matrix upper right corner; Fig. 1A, 1B). We used the software Unified Life Models (ULM; (Legendre & Clobert, 1995) to evaluate the matrix model and to calculate population growth rate, stable age distribution, reproductive value, and sensitivity and elasticity matrices. We summed elasticity values across all stages for the three demographic processes: fecundity (f), growth (sm, transition from one age stage to another), and stasis (ss, survival without transitioning). Our matrix transition model differed from the matrix transition model generated by Beston (2011), which used nine life stages. To ensure an accurate comparison between the two models, we combined the nine life stages from the matrix transition model in the meta-analysis (Beston, 2011) into five broader stages: cub, yearling, subadult, adult, and old adult. We selected five life stages due to the assumption that age might influence reproductive output, a pattern supported by research on other mammals (Hilderbrand et al., 2019; Nussey et al., 2008; Promislow & Harvey, 1990).

About this dataset

Context

The dataset tabulates the data for the Red Level, AL population pyramid, which represents the Red Level population distribution across age and gender, using estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. It lists the male and female population for each age group, along with the total population for those age groups. Higher numbers at the bottom of the table suggest population growth, whereas higher numbers at the top indicate declining birth rates. Furthermore, the dataset can be utilized to understand the youth dependency ratio, old-age dependency ratio, total dependency ratio, and potential support ratio.

Key observations

• Youth dependency ratio, which is the number of children aged 0-14 per 100 persons aged 15-64, for Red Level, AL, is 34.5.
• Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for Red Level, AL, is 23.7.
• Total dependency ratio for Red Level, AL is 58.2.
• Potential support ratio, which is the number of youth (working age population) per elderly, for Red Level, AL is 4.2.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group for the Red Level population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Red Level for the selected age group is shown in the following column.
• Population (Female): The female population in the Red Level for the selected age group is shown in the following column.
• Total Population: The total population of the Red Level for the selected age group is shown in the following column.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Red Level Population by Age. You can refer the same here

Footnotes: 1 Population estimates based on the Standard Geographical Classification (SGC) 2016 as delineated in the 2016 Census. 2 A census metropolitan area (CMA) or a census agglomeration (CA) is formed by one or more adjacent municipalities centred on a population centre (known as the core). A CMA must have a total population of at least 100,000 of which 50,000 or more must live in the core based on adjusted data from the previous Census of Population Program. A CA must have a core population of at least 10,000 also based on data from the previous Census of Population Program. To be included in the CMA or CA, other adjacent municipalities must have a high degree of integration with the core, as measured by commuting flows derived from data on place of work from the previous Census Program. If the population of the core of a CA falls below 10,000, the CA is retired from the next census. However, once an area becomes a CMA, it is retained as a CMA even if its total population declines below 100,000 or the population of its core falls below 50,000. All areas inside the CMA or CA that are not population centres are rural areas. When a CA has a core of at least 50,000, based on data from the previous Census of Population, it is subdivided into census tracts. Census tracts are maintained for the CA even if the population of the core subsequently falls below 50,000. All CMAs are subdivided into census tracts (2016 Census Dictionary, catalogue number 98-301-X2016001). 3 An area outside census metropolitan areas and census agglomerations is made up of all areas (within a province or territory) unallocated to a census metropolitan area (CMA) or census agglomeration (CA). 4 The population growth, which is used to calculate population estimates of census metropolitan areas and census agglomerations (table 17100135), is comprised of the components of population growth (table 17100136). 5 This table replaces table 17100079. 6 The components of population growth for census metropolitan areas (CMAs) and census agglomerations (CAs) sometimes had to be calculated using information at the census division level, using the geographic conversion method. This method involves using the population component calculated at the level of the CD(s) in which the CMA or CA is located and applying a ratio corresponding to the proportion of the CMA or CA population included in the corresponding CD(s). For periods prior to 2005/2006, all demographic components for all CMAs and CAs were calculated using geographic conversions. For the periods from 2005/2006 to 2010/2011 inclusively, emigration and internal migration components for areas that were not CMAs according to the 2011 SGC were calculated using geographic conversions. For the periods 2011/2012 to 2015/2016 inclusively, the emigration and internal migration components of regions that were not CMAs or CAs according to the 2011 SGC were calculated using geographic conversions. For the relevant demographic components, trends should be interpreted with caution where the method of calculation has changed over time. This caveat applies particularly to the intraprovincial migration component, for which the assumptions of the geographic conversion method are more at risk of not being met. 7 Period from July 1 to June 30. 8 Age on July 1. 9 The estimates for net intraprovincial migration are preliminary for 2020/2021 and final up to 2019/2020. For Quebec’s census metropolitan areas and census agglomerations, preliminary and final data (from 2001/2002) are taken from the estimates of l'Institut de la statistique du Québec" (ISQ). In rare instances it may have been necessary to modify ISQ’s estimates in order to avoid generating negative populations with the cohort component approach. For all census agglomerations (with the exception of those listed hereinafter) the method to calculate intraprovincial migration is not the same for periods starting in 2011/2012 and for past periods. For the following census agglomerations the method is not the same for periods starting in 2016/2017: Gander (N.L.) Sainte-Marie (Que.) Arnprior (Ont.) Carleton Place (Ont.) Wasaga Beach (Ont.) Winkler (Man.) Weyburn (Sask.) Nelson (B.C.). Thus historical trends for intraprovincial migration must be interpreted with caution for census agglomerations."

Footnotes: 1 Population estimates based on the Standard Geographical Classification (SGC) 2016 as delineated in the 2016 Census.2 A census metropolitan area (CMA) or a census agglomeration (CA) is formed by one or more adjacent municipalities centred on a population centre (known as the core). A CMA must have a total population of at least 100,000 of which 50,000 or more must live in the core based on adjusted data from the previous Census of Population Program. A CA must have a core population of at least 10,000 also based on data from the previous Census of Population Program. To be included in the CMA or CA, other adjacent municipalities must have a high degree of integration with the core, as measured by commuting flows derived from data on place of work from the previous Census Program. If the population of the core of a CA falls below 10,000, the CA is retired from the next census. However, once an area becomes a CMA, it is retained as a CMA even if its total population declines below 100,000 or the population of its core falls below 50,000. All areas inside the CMA or CA that are not population centres are rural areas. When a CA has a core of at least 50,000, based on data from the previous Census of Population, it is subdivided into census tracts. Census tracts are maintained for the CA even if the population of the core subsequently falls below 50,000. All CMAs are subdivided into census tracts (2016 Census Dictionary, catalogue number 98-301-X2016001).3 An area outside census metropolitan areas and census agglomerations is made up of all areas (within a province or territory) unallocated to a census metropolitan area (CMA) or census agglomeration (CA).4 The population growth, which is used to calculate population estimates of census metropolitan areas and census agglomerations (table 17100135), is comprised of the components of population growth (table 17100136).5 This table replaces table 17100079.6 The components of population growth for census metropolitan areas (CMAs) and census agglomerations (CAs) sometimes had to be calculated using information at the census division level, using the geographic conversion method. This method involves using the population component calculated at the level of the CD(s) in which the CMA or CA is located and applying a ratio corresponding to the proportion of the CMA or CA population included in the corresponding CD(s). For periods prior to 2005/2006, all demographic components for all CMAs and CAs were calculated using geographic conversions. For the periods from 2005/2006 to 2010/2011 inclusively, emigration and internal migration components for areas that were not CMAs according to the 2011 SGC were calculated using geographic conversions. For the periods 2011/2012 to 2015/2016 inclusively, the emigration and internal migration components of regions that were not CMAs or CAs according to the 2011 SGC were calculated using geographic conversions. For the relevant demographic components, trends should be interpreted with caution where the method of calculation has changed over time. This caveat applies particularly to the intraprovincial migration component, for which the assumptions of the geographic conversion method are more at risk of not being met.7 Period from July 1 to June 30.8 Age on July 1.9 The estimates for immigrants are preliminary for 2020/2021 and final up to 2019/2020.

Footnotes: 1 Population estimates based on the Standard Geographical Classification (SGC) 2016 as delineated in the 2016 Census. 2 A census metropolitan area (CMA) or a census agglomeration (CA) is formed by one or more adjacent municipalities centred on a population centre (known as the core). A CMA must have a total population of at least 100,000 of which 50,000 or more must live in the core based on adjusted data from the previous Census of Population Program. A CA must have a core population of at least 10,000 also based on data from the previous Census of Population Program. To be included in the CMA or CA, other adjacent municipalities must have a high degree of integration with the core, as measured by commuting flows derived from data on place of work from the previous Census Program. If the population of the core of a CA falls below 10,000, the CA is retired from the next census. However, once an area becomes a CMA, it is retained as a CMA even if its total population declines below 100,000 or the population of its core falls below 50,000. All areas inside the CMA or CA that are not population centres are rural areas. When a CA has a core of at least 50,000, based on data from the previous Census of Population, it is subdivided into census tracts. Census tracts are maintained for the CA even if the population of the core subsequently falls below 50,000. All CMAs are subdivided into census tracts (2016 Census Dictionary, catalogue number 98-301-X2016001). 3 An area outside census metropolitan areas and census agglomerations is made up of all areas (within a province or territory) unallocated to a census metropolitan area (CMA) or census agglomeration (CA). 4 The population growth, which is used to calculate population estimates of census metropolitan areas and census agglomerations (table 17100135), is comprised of the components of population growth (table 17100136). 5 This table replaces table 17100079. 6 The components of population growth for census metropolitan areas (CMAs) and census agglomerations (CAs) sometimes had to be calculated using information at the census division level, using the geographic conversion method. This method involves using the population component calculated at the level of the CD(s) in which the CMA or CA is located and applying a ratio corresponding to the proportion of the CMA or CA population included in the corresponding CD(s). For periods prior to 2005/2006, all demographic components for all CMAs and CAs were calculated using geographic conversions. For the periods from 2005/2006 to 2010/2011 inclusively, emigration and internal migration components for areas that were not CMAs according to the 2011 SGC were calculated using geographic conversions. For the periods 2011/2012 to 2015/2016 inclusively, the emigration and internal migration components of regions that were not CMAs or CAs according to the 2011 SGC were calculated using geographic conversions. For the relevant demographic components, trends should be interpreted with caution where the method of calculation has changed over time. This caveat applies particularly to the intraprovincial migration component, for which the assumptions of the geographic conversion method are more at risk of not being met. 7 Period from July 1 to June 30. 8 Age on July 1. 9 The estimates for net non-permanent residents are preliminary for 2020/2021, updated for 2019/2020 final up to 2018/2019. 10 Non-permanent residents (NPRs) are persons who are lawfully in Canada on a temporary basis under the authority of a temporary resident permit, along with members of their family living with them. NPRs include foreign workers, foreign students, the humanitarian population and other temporary residents. The humanitarian population includes refugee claimants and temporary residents who are allowed to remain in Canada on humanitarian grounds and are not categorized as either foreign workers or foreign students.