In 2023, the annual population growth in Haiti increased by 0.03 percentage points (+2.65 percent) compared to 2022. This was the first time during the observed period that the population growth has increased in Haiti. Annual population growth refers to the change in the population over time, and is affected by factors such as fertility, mortality, and migration.Find more key insights for the annual population growth in countries like Jamaica and St. Vincent and the Grenadines.

The annual population growth in Mali saw no significant changes in 2023 in comparison to the previous year 2022 and remained at around 2.97 percent. Still, 2023 marked the second consecutive decline of the population growth. Population growth deals with the annual change in total population, and is affected by factors such as fertility, mortality, and migration.Find more key insights for the annual population growth in countries like Senegal and Niger.

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

The annual population growth in Jamaica declined to 0.02 percent in 2023. In 2023, the population growth thereby reached its lowest value in recent years. Notably, the population growth is continuously decreasing over the last years.Annual population growth refers to the change in the population over time, and is affected by factors such as fertility, mortality, and migration.Find more key insights for the annual population growth in countries like Cuba and Puerto Rico.

The annual population growth in Finland increased by 0.2 percentage points (+74.07 percent) compared to the previous year. With 0.5 percent, the population growth thereby reached its highest value in the observed period. Annual population growth refers to the change in the population over time, and is affected by factors such as fertility, mortality, and migration.Find more key insights for the annual population growth in countries like Sweden and Faroe Islands.

The annual population growth in Latin America & the Caribbean increased slightly to 0.7 percent since the previous year. Still, 2023 marks the lowest population growth during the observed period. Population growth deals with the annual change in total population, and is affected by factors such as fertility, mortality, and migration.

In 2023, the annual population growth in Indonesia increased by 0.1 percentage points (+13.33 percent) compared to 2022. In total, the population growth amounted to 0.84 percent in 2023. Annual population growth refers to the change in the population over time, and is affected by factors such as fertility, mortality, and migration.Find more key insights for the annual population growth in countries like Myanmar and Malaysia.

Background: The Environmental Quality Index (EQI) reflects environmental performance and sustainability, with DKI Jakarta scoring 54.57—below its target. This study analyzes the influence of the Human Development Index (HDI), population growth, and the Information, Communication, and Technology Development Index (IDI) on DKI Jakarta’s EQI. Methods: A quantitative approach using time-series data (2008–2023) and multiple linear regression analysis was applied to evaluate the relationship between HDI, population growth, and IDI with environmental quality. Findings: HDI positively impacts environmental quality, contributing 5.776%. In contrast, a 1% increase in IDI and population growth correlates with a 2.183% and 173.456% decline in EQI, respectively, highlighting the environmental challenges of urbanization and technological expansion. Conclusion: Improving human resources, adopting green technologies, and fostering collaboration among stakeholders are critical to enhancing environmental quality. Novelty/Originality of this article: This study provides new insights into the interplay of HDI, IDI, and population growth in influencing environmental quality in a major urban area.

Plant demography is a function of both the vital rate characteristics of a species (i.e., survival, growth, and reproduction) and the environmental factors that interact with them to create population dynamics. A more detailed understanding of how local-scale environmental factors and variation in individual vital rates shape population-level demographic patterns is needed to improve predictions of population responses to environmental change and implement successful plant conservation strategies. In this study, we examined how individual vital rates for Shortia galacifolia, an endangered, evergreen herb endemic to the southern Blue Ridge Mountains, USA, change as a function of individual size and resource availability and how that variation affects Shortia demography at four sites representing natural and introduced populations using integral projection models (IPMs). We found that Shortia population growth is positively related to individual size and soil moisture. Changes in soil moisture availability altered the importance of survival and growth in predicting Shortia demography but did not affect the contribution of asexual reproduction for most sites. Moreover, changes in vital rate contributions under a low soil moisture scenario were limited to introduced populations growing outside Shortia’s natural climate envelope. Our study underscores the importance of quantifying the influence of individual state characteristics and environmental variables on different vital rates among natural and introduced populations and demonstrates how the combination of these factors can contribute to the success or failure of rare plant populations.

The annual population growth in Mauritius increased by 0.2 percentage points in 2023. This was a significant increase in the population growth. Nevertheless, the last two years recorded a significantly lower population growth than the preceding years.Population growth deals with the annual change in total population, and is affected by factors such as fertility, mortality, and migration.Find more key insights for the annual population growth in countries like Eritrea and Ethiopia.

LifeTablesLife tables for 24 species of terrestrial vertebrates.

Six metrics were used to determine Population Vulnerability: global population size, annual occurrence in the California Current System (CCS), percent of the population present in the CCS, threat status, breeding score, and annual adult survival. Global Population size (POP)—to determine population size estimates for each species we gathered information tabulated by American Bird Conservancy, Birdlife International, and other primary sources. Proportion of Population in CCS (CCSpop)—for each species, we generated the population size within the CCS by averaging region-wide population estimates, or by combining state estimates for California, Oregon, and Washington for each species (if estimates were not available for a region or state, “NA” was recorded in place of a value) and then dividing the CCSpop value by the estimated global population size (POP) to yield the percentage of the population occurring in the CCS. Annual Occurrence in the CCS (AO)—for each species, we estimated the number of months per year within the CCS and binned this estimate into three categories: 1–4 months, 5–8 months, or 9–12 months. Threat Status (TS)—for each species, we used the International Union for Conservation of Nature (IUCN) species threat status (IUCN 2014) and the U.S. Fish and Wildlife national threat status lists (USFWS 2014) to determine TS values for each species. If available, we also evaluated threat status values from state and international agencies. Breeding Score (BR)—we determined the degree to which a species breeds and feeds its young in the CCS according to 3 categories: breeds in the CCS, may breed in the CCS, or does not breed in the CCS. Adult Survival (AS)—for each species, we referenced information to estimate adult annual survival, because adult survival among marine birds in general is the most important demographic factor that can affect population growth rate and therefore inform vulnerability. These data support the following publication: Adams, J., Kelsey, E.C., Felis J.J., and Pereksta, D.M., 2016, Collision and displacement vulnerability among marine birds of the California Current System associated with offshore wind energy infrastructure: U.S. Geological Survey Open-File Report 2016-1154, 116 p., https://doi.org/10.3133/ofr20161154. These data were revisied in June 2017 and the revision published in August 2017. Please be advised to use CCS_vulnerability_FINAL_VERSION_v9_PV.csv

The annual population growth in Guatemala declined to 1.4 percent in 2023. The population growth thereby reached its lowest value in recent years. Annual population growth refers to the change in the population over time, and is affected by factors such as fertility, mortality, and migration.Find more key insights for the annual population growth in countries like Panama and Honduras.

Changes in life history traits of species can be an important indicator of potential factors influencing populations. For grizzly bears (Ursus arctos) in the Greater Yellowstone Ecosystem (GYE), recent decline of whitebark pine (WBP; Pinus albicaulis), an important fall food resource, has been paired with a slowing of population growth following two decades of robust population increase. These observations have raised questions whether resource decline or density-dependent processes may be associated with changes in population growth. Distinguishing these effects based on changes in demographic rates can be difficult. However, unlike the parallel demographic responses expected from both decreasing food availability and increasing population density, we hypothesized opposing behavioral responses of grizzly bears with regard to changes in home-range size. We used the dynamic changes in food resources and population density of grizzly bears as a natural experiment to examine hypotheses regarding these potentially competing influences on grizzly bear home-range size. We found that home-range size did not increase during the period of whitebark pine decline and was not related to proportion of whitebark pine in home ranges. However, female home-range size was negatively associated with an index of population density. Our data indicate that home-range size of grizzly bears in the GYE is not associated with availability of WBP, and, for female grizzly bears, increasing population density may constrain home-range size.

Heat waves are transient environmental events but can have lasting impacts on populations through lethal and sub-lethal effects on demographic vital rates. Sub-lethal temperature stress affects individual energy balance, potentially affecting individual fitness and population growth. Environmental temperature can, however, have distinct effects on different life-history traits, and the net effect of short-term temperature stress on population growth may lead to different population responses over different time frames. Furthermore, sublethal temperature responses may be density dependent, leading to potentially complicated feedbacks between heat stress and demographic responses over time. Here, we test the hypotheses that: (i) populations subjected to higher heat wave temperatures and longer heat wave durations are more negatively affected than those subjected to less intense and shorter heat waves, (ii) heat wave effects are more pronounced during density-dependent population growth phases, and (iii) population density patterns over time mirror the short-term population growth rate responses. We subjected experimental populations of the marine copepod Tigriopus californicus to short-term heat stress perturbations ("heat waves") at two different time points during a 100-day period. Overall, we found that population growth rates and density responded similarly (and moderately) to heat wave intensity and duration, and that the heat wave effects on populations were largely density-dependent. We detected heat wave effects on population growth and density at low densities, but not at high densities. At low densities, we found that population growth declined with heat wave duration for the more intense heat wave intensity group, but did not detect an effect of heat wave duration within the less intense heat wave intensity group. Our study demonstrates that while ephemeral density-independent factors can influence population vital rates, understanding the longer-term consequences of transient perturbations on populations requires understanding these effects in the context of density dependence and its relationship to temperature. Higher densities may buffer the negative effects of intense heat waves and confer some degree of resilience.

Current and future projected waste generation in the greater Maputo area, by city.

1. Forest cover has increased world-wide over the last decade despite continuous forest fragmentation. However, a lack of long-term demographic data hinders our understanding of the spatial dynamics of colonization in remnant populations inhabiting recently protected areas or set-aside rural lands. 2. We investigated the population expansion of the Phoenician juniper (Juniperus phoenicea subsp. turbinata), which is an endozoochorous Mediterranean tree species inhabiting landscapes that have been managed for many centuries. By combining the photointerpretation of aerial photos that have been taken over the last 50 years with in situ sampling and spatial analyses of replicated plots, we estimated the population growth over the chronosequence; identified hotspots, coldspots and outliers of regeneration; and assessed the roles of key environmental factors in driving demographic expansion patterns, including elevation, initial density and distance to remnant forests. 3. Ecological factors leading to seed limitation, such as initial plant density, are expected to drive colonization patterns at the early stages. Factors mediating the competition for limiting resources, such as water availability, would prevail at later stages of expansion. We further expect that nucleated colonization patterns emerge driven by vertebrate seed dispersal. 4. The photointerpretation of aerial images in combination with in situ measurements has yielded reliable density data. Overall, our results show a marked demographic expansion during the first decade followed by a period of steady and heterogeneous population growth with signs of local population decline. We found evidence of nucleated establishment patterns as expected for an endozoochorous species. Hotspots and outliers of regeneration emerged throughout the study chronosequence, whereas coldspots of regeneration only appeared at advanced colonization stages. Factors influencing dispersal limitation had contrasting effects at different colonization stages, and the initial density influenced population growth at various spatial scales. 5. Synthesis. The photointerpretation of aerial images shows that the influence of dispersal limitation versus factors mediating competitive responses changes throughout colonization stages. Whereas dispersal limitation is the main factor influencing colonization at early stages, competition for local resources controls population growth at later stages. Therefore, long-term studies are required to capture the overall combined influence of key ecological factors in shaping long-term spatial demographic trends.

Association results between non genetic factors and the PGF levels according to the best fitting models of the Cilento and Denmark samples. (DOCX)

1. Migration counts are popular indices used to monitor population trends over time. Advanced analytical methods for estimating abundance of unmarked, open populations now incorporate population growth models and simultaneously test for covariate effects on abundance and detection probability. However, estimating population abundance at a staging site is complicated by daily immigration and emigration of unmarked individuals.
2. We applied a set of generalized N-mixture models to simulated count data to test their applicability for transient populations. Using simulated datasets, parameters were unbiased when the apparent survival rate varied within a season or was mis-specified in a model, but not when the immigration or detection probability was mis-specified. 3. With knowledge from the simulated data, we applied these models to daily counts of staging migratory shorebirds and estimated daily abundances accounting for variation in the detection and immigration rates. Daily counts of ruddy turnstones (Arenaria interpres) staging at Westhampton Island, New York, were collected during northward migration (1997–1999). We tested the effects of weather and tides on detection probability, and we modeled within-season variation in immigration rates as a function of time. 4. Covariates affecting the detection probability differed among years, but tide height consistently was correlated with detection probability. Accounting for detection and immigration rates, the predicted maximum single-day populations of ruddy turnstones were 172%, 165%, and 129% of the observed counts for each year. 5. Synthesis and applications. Management and conservation plans for migratory species require abundance estimates that are near the true population size though they are difficult to obtain. Our study is the first empirical application of the generalized N-mixture model that incorporates temporal trends in immigration and estimates daily abundance of a staging unmarked migratory population. Correct estimation of population sizes and the environmental factors affecting them can aid the conservation prioritization of species and staging sites. Moreover, the use of generalized N-mixture models can improve our understanding of the environmental factors that shape migratory movements.

The fertility rate of a married woman can be measured by the length of the first birth interval (FBI). This length is influenced by some significant factors. Better knowledge about the factors affecting the birth interval can help in controlling population growth and fertility progress. The main focus of this study was to compare the performance of Cox-Proportional Hazard (Cox-PH) and the parametric Accelerated Failure Time (AFT) model in assessing the impact of significant factors affecting the time to FBI of ever-married Bangladeshi women. Information of 14941 women having at least one birth was included in this study from the most recent nationally representative data 2017–18 Bangladesh Demographic and Health Survey (BDHS). We used the Cox-PH model and AFT model under various parametric forms of survival time distributions (Weibull, Exponential, and Log-normal distribution) to measure the effect of factors influencing FBI. And then, a respective Akaike information criterion (AIC) was calculated for selecting the best-fitted model. According to the AIC and BIC values, the log-normal model fitted better than other AFT models. Based on the log-normal model, women’s age and age at first marriage, maternal and paternal education, contraceptive use status, used anything to avoid pregnancy, sex of household head, and spousal age difference had a significant association with FBI of ever married Bangladeshi women. The parametric AFT model (log-normal distribution) was a better fitted model in evaluating the covariates associated with FBI of ever-married Bangladeshi Women. Higher education, the right age at marriage, and proper knowledge about family planning (i.e., contraception use) should be ensured for every married person to control the gap of the first birth.