In 2023, the annual population growth in the United States stood at 0.49 percent. Between 1961 and 2023, the figure dropped by 1.17 percentage points, though the decline followed an uneven course rather than a steady trajectory.

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 world's population first reached one billion people in 1805, and reached eight billion in 2022, and will peak at almost 10.2 billion by the end of the century. Although it took thousands of years to reach one billion people, it did so at the beginning of a phenomenon known as the demographic transition; from this point onwards, population growth has skyrocketed, and since the 1960s the population has increased by one billion people every 12 to 15 years. The demographic transition sees a sharp drop in mortality due to factors such as vaccination, sanitation, and improved food supply; the population boom that follows is due to increased survival rates among children and higher life expectancy among the general population; and fertility then drops in response to this population growth. Regional differences The demographic transition is a global phenomenon, but it has taken place at different times across the world. The industrialized countries of Europe and North America were the first to go through this process, followed by some states in the Western Pacific. Latin America's population then began growing at the turn of the 20th century, but the most significant period of global population growth occurred as Asia progressed in the late-1900s. As of the early 21st century, almost two-thirds of the world's population lives in Asia, although this is set to change significantly in the coming decades. Future growth The growth of Africa's population, particularly in Sub-Saharan Africa, will have the largest impact on global demographics in this century. From 2000 to 2100, it is expected that Africa's population will have increased by a factor of almost five. It overtook Europe in size in the late 1990s, and overtook the Americas a few years later. In contrast to Africa, Europe's population is now in decline, as birth rates are consistently below death rates in many countries, especially in the south and east, resulting in natural population decline. Similarly, the population of the Americas and Asia are expected to go into decline in the second half of this century, and only Oceania's population will still be growing alongside Africa. By 2100, the world's population will have over three billion more than today, with the vast majority of this concentrated in Africa. Demographers predict that climate change is exacerbating many of the challenges that currently hinder progress in Africa, such as political and food instability; if Africa's transition is prolonged, then it may result in further population growth that would place a strain on the region's resources, however, curbing this growth earlier would alleviate some of the pressure created by climate change.

United States US: Population: Growth data was reported at 0.713 % in 2017. This records a decrease from the previous number of 0.734 % for 2016. United States US: Population: Growth data is updated yearly, averaging 0.979 % from Dec 1960 (Median) to 2017, with 58 observations. The data reached an all-time high of 1.702 % in 1960 and a record low of 0.711 % in 2013. United States US: Population: Growth data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United States – Table US.World Bank.WDI: Population and Urbanization Statistics. Annual population growth rate for year t is the exponential rate of growth of midyear population from year t-1 to t, expressed as a percentage . Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship.; ; Derived from total population. Population source: (1) United Nations Population Division. World Population Prospects: 2017 Revision, (2) Census reports and other statistical publications from national statistical offices, (3) Eurostat: Demographic Statistics, (4) United Nations Statistical Division. Population and Vital Statistics Reprot (various years), (5) U.S. Census Bureau: International Database, and (6) Secretariat of the Pacific Community: Statistics and Demography Programme.; Weighted average;

The annual population growth in Serbia increased by 1.9 percentage points in 2023. This was a significant increase in 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 Montenegro and Bosnia & Herzegovina.

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 the United States increased by 0.1 percentage points (+27.03 percent) in 2023. In total, the population growth amounted to 0.49 percent in 2023. 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 Mexico and Canada.

Invasive species offer ecologists the opportunity to study the factors governing species distributions and population growth. The Eurasian Collared-Dove (Streptopelia decaocto) serves as a model organism for invasive spread because of the wealth of abundance records and the recent development of the invasion. We tested whether a set of environmental variables were related to the carrying capacities and growth rates of individual populations by modeling the growth trajectories of individual populations of the Collared-Dove using Breeding Bird Survey (BBS) and Christmas Bird Count (CBC) data. Depending on the fit of our growth models, carrying capacity and growth rate parameters were extracted and modeled using historical, geographical, land cover and climatic predictors. Model averaging and individual variable importance weights were used to assess the strength of these predictors. The specific variables with the greatest support in our models differed between data sets, which may be the result of temporal and spatial differences between the BBS and CBC. However, our results indicate that both carrying capacity and population growth rates are related to developed land cover and temperature, while growth rates may also be influenced by dispersal patterns along the invasion front. Model averaged multivariate models explained 35–48% and 41–46% of the variation in carrying capacities and population growth rates, respectively. Our results suggest that widespread species invasions can be evaluated within a predictable population ecology framework. Land cover and climate both have important effects on population growth rates and carrying capacities of Collared-Dove populations. Efforts to model aspects of population growth of this invasive species were more successful than attempts to model static abundance patterns, pointing to a potentially fruitful avenue for the development of improved invasive distribution models.

The annual population growth in Peru increased by 0.1 percentage points (+10.42 percent) compared to the previous year. While the population growth increased significantly in the first phase of the observed period, the increase slowed down in the last 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 Ecuador and Bolivia.

How demographic factors lead to variation or change in growth rates can be investigated using life table response experiments (LTRE) based on structured population models. Traditionally, LTREs focused on decomposing the asymptotic growth rate, but more recently decompositions of annual ‘realized’ growth rates have gained in popularity. Realized LTREs have been used particularly to understand how variation in vital rates translates into variation in growth for populations under long-term study. For these, complete population models may be constructed by combining data in an integrated population model (IPM). IPMs are also used to investigate how temporal variation in environmental drivers affect vital rates. Such investigations have usually come down to estimating covariate coefficients for the effects of environmental variables on vital rates, but formal ways of assessing how they lead to variation in growth rates have been lacking. We extend realized LTREs in two ways. First, we furt..., , The data are in the form of .Rdata files that can be opened with the R software.

Datasets archived here consist of all data analyzed in Duan et al. 2015 from Journal of Applied Ecology. Specifically, these data were collected from annual sampling of emerald ash borer (Agrilus planipennis) immature stages and associated parasitoids on infested ash trees (Fraxinus) in Southern Michigan, where three introduced biological control agents had been released between 2007 - 2010. Detailed data collection procedures can be found in Duan et al. 2012, 2013, and 2015. Resources in this dataset:Resource Title: Duan J Data on EAB larval density-bird predation and unknown factor from Journal of Applied Ecology. File Name: Duan J Data on EAB larval density-bird predation and unknown factor from Journal of Applied Ecology.xlsxResource Description: This data set is used to calculate mean EAB density (per m2 of ash phloem area), bird predation rate and mortality rate caused by unknown factors and analyzed with JMP (10.2) scripts for mixed effect linear models in Duan et al. 2015 (Journal of Applied Ecology).Resource Title: DUAN J Data on Parasitism L1-L2 Excluded from Journal of Applied Ecology. File Name: DUAN J Data on Parasitism L1-L2 Excluded from Journal of Applied Ecology.xlsxResource Description: This data set is used to construct life tables and calculation of net population growth rate of emerald ash borer for each site. The net population growth rates were then analyzed with JMP (10.2) scripts for mixed effect linear models in Duan et al. 2015 (Journal of Applied Ecology).Resource Title: DUAN J Data on EAB Life Tables Calculation from Journal of Applied Ecology. File Name: DUAN J Data on EAB Life Tables Calculation from Journal of Applied Ecology.xlsxResource Description: This data set is used to calculate parasitism rate of EAB larvae for each tree and then analyzed with JMP (10.2) scripts for mixed effect linear models on in Duan et al. 2015 (Journal of Applied Ecology).Resource Title: READ ME for Emerald Ash Borer Biocontrol Study from Journal of Applied Ecology. File Name: READ_ME_for_Emerald_Ash_Borer_Biocontrol_Study_from_Journal_of_Applied_Ecology.docxResource Description: Additional information and definitions for the variables/content in the three Emerald Ash Borer Biocontrol Study tables: Data on EAB Life Tables Calculation Data on EAB larval density-bird predation and unknown factor Data on Parasitism L1-L2 Excluded from Journal of Applied Ecology Resource Title: Data Dictionary for Emerald Ash Borer Biocontrol Study from Journal of Applied Ecology. File Name: AshBorerAnd Parasitoids_DataDictionary.csvResource Description: CSV data dictionary for the variables/content in the three Emerald Ash Borer Biocontrol Study tables: Data on EAB Life Tables Calculation Data on EAB larval density-bird predation and unknown factor Data on Parasitism L1-L2 Excluded from Journal of Applied Ecology Fore more information see the related READ ME file.

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.

Isolation caused by anthropogenic habitat fragmentation can destabilize populations. Populations relying on the inflow of immigrants can face reduced fitness due to inbreeding depression as fewer new individuals arrive. Empirical studies of the demographic consequences of isolation are critical to understanding how populations persist through changing conditions. We used a 34-year demographic and environmental dataset from a population of cooperatively-breeding Florida Scrub-Jays (Aphelocoma coerulescens) to create mechanistic models linking environmental and demographic factors to population growth rates. We found that the population has not declined despite both declining immigration and increasing inbreeding, owing to a coinciding response in breeder survival. We find evidence of density-dependent immigration, breeder survival, and fecundity, indicating that interactions between vital rates and local density play a role in buffering the population against change. Our study elucidates..., All work was approved by the Cornell University Institutional Animal Care and Use Committee (IACUC 2010-0015) and authorized by permits from the US Fish and Wildlife Service (TE824723-8), the US Geological Survey (banding permit 07732), and the Florida Fish and Wildlife Conservation Commission (LSSC-10-00205)., , # Density dependence maintains long-term stability despite increased isolation and inbreeding in the Florida Scrub-Jay

https://doi.org/10.5061/dryad.p2ngf1vz3

This dataset contains raw census data (FullLOI.txt), derived vital rates (vr_clean_F_4stageDemo.rdata, vr_clean_M_4stageDemo.rdata), ecological metrics (reqsoi_update.txt, acorns_update.txt, TerrYrBurnArea.txt, TerrMap.txt, TerrsToKeep.txt, densityCalcDemo.rdata, env_var_updateDemo.txt, envFac_annual.txt), pedigree information (pedInbr.txt, kinship_coef_Demo.rdata), and demographic models created using these data (vr_modelsDemo_revision_20240518.rdata, vr_modelsDemo.rdata, Demo_LTRE_results_20240518.rdata), including model validation results (vr_modelsDemo_validation_revisions_20240518.rdata).

Description of the data and file structure

FullLOI.txt

• USFWS: Individual ID
• Year: Year of census
• TerrYr: Te...

Comp. VA (or VA) and Comp. VP (or VP) are the competitor's additive genetic and phenotypic variance, respectively; dE/dt is the rate of environmental change.

Components of population growth, annual: births, deaths, immigrants, emigrants, returning emigrants, net temporary emigrants, net interprovincial migration, net non-permanent residents, residual deviation.

Abstract: Dengue virus transmission has increased over the last four decades seemingly due to changes in climate, urbanization and population growth. Using estimates of dengue transmission suitability based on historical temperature and humidity data, we examined how shifts in these climatic variables and human population growth have contributed to the change in the geographical distribution and size of the global population living in areas with high climate suitability from 1979 to 2022. We found an expansion in climate suitability in North America, East Asia and the Mediterranean basin, where with few exceptions, endemicity is not yet established. Globally, we estimated that the population in areas with high climate suitability has grown by approximately 2.5 billion. In the Global South, this increase was largely driven by population growth in areas with historically favorable climate suitability, while in the Global North this increase predominantly occurred in previously unfavorable areas with limited population growth.This dataset includes the following supplementary data: Supplementary Table 1 (supplementary_table1.xlsx): Estimated changes in land area with high climate suitability for dengue virus transmission from 1979-1983 to 2018-2022. Supplementary Table 2 (supplementary_table2.xlsx): Estimated changes in population living in area with high dengue virus transmission suitability from 1979-1983 to 2018-2022. Supplementary Table 3 (supplementary_table3.xlsx): Categorization of countries and territories into the Global South and Global North from the United Nations Finance Center for South-South Cooperation.Supplementary Data (global_denv_suitability_trend.tif): Long-term trends in climate suitability for dengue virus transmission.

Multiple, simultaneous environmental changes, in climatic/abiotic factors, in interacting species, and in direct human influences, are impacting natural populations and thus biodiversity, ecosystem services, and evolutionary trajectories. Determining whether the magnitudes of the population impacts of abiotic, biotic, and anthropogenic drivers differ, accounting for their direct effects and effects mediated through other drivers, would allow us to better predict population fates and design mitigation strategies. We compiled 644 paired values of the population growth rate (lambda) from high and low levels of an identified driver from demographic studies of terrestrial plants. Among abiotic drivers, natural disturbance (not climate), and among biotic drivers, interactions with neighboring plants had the strongest effects on lambda. However, when drivers were combined into the three main types, their average effects on lambda did not differ. For the subset of studies that measured both the average and variability of the driver, lambda was more sensitive to one standard deviation of change in abiotic drivers relative to biotic drivers, but sensitivity to biotic drivers was still substantial. Similar impact magnitudes for abiotic/biotic/anthropogenic drivers holds for plants of different growth forms, for different latitudinal zones, and for biomes characterized by harsher or milder abiotic conditions, suggesting that all three drivers have equivalent impacts across a variety of contexts. Thus the best available information about the integrated effects of drivers on all demographic rates provides no justification for ignoring drivers of any of these three types when projecting ecological and evolutionary responses of populations and of biodiversity to environmental changes.

Methods The main data consist of pairs of estimates of population growth rates of terrestrial plants, one from a relatively high and one from a relatively low level of an identified environmental driver (i.e., a factor such as climate, soil, interactions with competitors, herbivores, pathogens, or pollinators, or anthropogenic impacts). These estimates were taken from published studies. When available, levels of the environmental driver are also included, along with meta-data from each site (e.g., publication citation, species name, geographical location, etc.).

Dataset S1 includes the full data extracted from the data sources; see

Dataset S2 includes all data used in the statistical analyses, based on the full data from Dataset S1; see

The file gives the full citations for the papers in Datasets S1 and S2.

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

The annual population growth in Argentina increased by 0.1 percentage points (+47.62 percent) in 2023. In total, the population growth amounted to 0.29 percent in 2023. This increase was preceded by a declining 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 Paraguay and Uruguay.

Approximately 25% of mammals are currently threatened with extinction, a risk that is amplified under climate change. Species persistence under climate change is determined by the combined effects of climatic factors on multiple demographic rates (survival, development, reproduction), and hence, population dynamics. Thus, to quantify which species and regions on Earth are most vulnerable to climate-driven extinction, a global understanding of how different demographic rates respond to climate is urgently needed. Here, we perform a systematic review of literature on demographic responses to climate, focusing on terrestrial mammals, for which extensive demographic data are available. To assess the full spectrum of responses, we synthesize information from studies that quantitatively link climate to multiple demographic rates. We find only 106 such studies, corresponding to 87 mammal species. These 87 species constitute < 1% of all terrestrial mammals. Our synthesis reveals a strong m..., For each mammal species i with available life-history information, we searched SCOPUS for studies (published before 2018) where the title, abstract, or keywords contained the following search terms:Â

Scientific species namei AND (demograph* OR population OR life-history OR "life history" OR model) AND (climat* OR precipitation OR rain* OR temperature OR weather) AND (surv* OR reprod* OR recruit* OR brood OR breed* OR mass OR weight OR size OR grow* OR offspring OR litter OR lambda OR birth OR mortality OR body OR hatch* OR fledg* OR productiv* OR age OR inherit* OR sex OR nest* OR fecund* OR progression OR pregnan* OR newborn OR longevity).

We used the R package taxize (Chamberlain and Szöcs 2013) to resolve discrepancies in scientific names or taxonomic identifiers and, where applicable, searched SCOPUS using all scientific names associated with a species in the Integrated Taxonomic Information System (ITIS; http://www.itis.gov).

We did not extract information on demographic-r..., ReadMe File uploaded